distance education meaning

Distance Learning: The Ultimate Guide to Online Learning

“Earn a college degree in your pajamas!” “Get your bachelor’s without leaving the house!” “Study wherever and however you want!”

Higher ed has been marketing online courses and degree programs like this for years now. Online programs are more flexible than the traditional residential university experience. But catchphrases like these cause students to think online study is easy and convenient, too. While the flexibility is real, know that college isn’t supposed to be easy, and it’s rarely convenient.

It’s important to begin the process of choosing a program with your eyes wide open. Don’t believe the marketing spin that this is going to be a cakewalk. Earning a degree is going to take serious effort and long-term commitment.

It’s also important to understand that not all programs are created equal. In fact, no two programs are exactly the same. There are wide variances in quality, format, cost, success rates, and more.

What is Distance Learning?

Distance learning is an educational process where students receive instruction through online classes, video recordings, video conferencing, or any other audio/visual technology medium. It enables people to receive education without having to be physically present in a classroom.

Properly designed distance learning programs can be a very convenient and effective way to acquire more education. This may seem difficult without students and teachers interacting in a classroom, but people enrolled in distance learning programs can learn just as much away from a classroom as in one.

Distance learning and education are interchangeable terms. Distance learning is not a recent phenomenon. The origins of distance learning can be traced back to the advent of the modern postal system and the mass production of printed publications, which made it possible to spread information rapidly throughout the world.

Why Distance Learning?

Distance learning has made education more accessible to larger groups of people. It is a convenient way to obtain work experience while completing college or other vocational training. Many organizations, such as the military, large corporations, and government agencies rely on distance education to train service members and employees. Education has also changed as communication technology has revolutionized society.

In most cases, education or specialized training is a requirement for most high paid jobs. The availability of the Internet has increased the number of online courses. These courses are offered at online colleges, such as Argosy University, University of Phoenix, Capella University, and Kaplan University.

Who Uses Distance Learning?

More students today are taking advantage of distance learning programs. Working professionals, high school students, and even traditional college students enroll in distance learning classes. Companies and other organizations frequently utilize distance learning programs to train employees. The following are some of the reasons people enroll in distance learning programs:

• Students living in rural areas or those unable to attend traditional classes utilize distance learning
• Students from all over the world can enroll in online courses offered at specific colleges
• Companies utilize distance learning programs to train employees, especially those working in distance regions

Distance learning is very flexible.

Although distance learning can fit into anyone’s schedule, students must take the initiative to study and complete their course work. Distance learning programs are not easy or automatic, so lazy students will probably not successfully complete courses they enroll in. However, even students that are busy or have numerous responsibilities should find the time to study because of the flexibility of these programs.

What technology is used for online distance learning?

Many different types of technology are utilized to enhance online learning. Special computer programs, high speed Internet, and webcam broadcasting technology are just a few of the modern technologies utilized in distance learning. As a result, learning opportunities that never existed for people living in distance or rural areas can obtain a college education or specialized job training. It’s not uncommon for a student living in a rural region of South Dakota to complete a course offered by a college in California.

Students often interact with teachers using video conferencing, satellite, and Internet technology. They can also communicate with other students enrolled in the same course using modern telecommunications technology.

Since students can complete courses wherever the Internet is accessible, many often take time during a work break or while staying in a hotel during a business trip to complete their school work. The flexibility of distance learning is one of the main appeals of these programs.

What is the experience like for online distance learners?

Since distance learning is slowly becoming a popular way to complete college or job training, many people still have reservations about it. The details provided below will give those considering distance learning an idea of what it’s like:

• Students usually interact with classmates and teachers in chat rooms and other instant messaging services. This makes it possible to ask questions and share comments without sitting in a classroom. Teacher lectures are frequently broadcast online, and many students and teachers stay in touch via conference calling technology.
• Group work is completed in chat rooms and special rooms on websites. Students also use e-mail, instant messaging, and web broadcasting technology to discuss project ideas with classmates.
• Course assignments completed by students are completed on a website or submitted as email attachments. Students are usually not permitted to submit work completed on websites after due dates.
• Most reference materials, such as documents and books, are accessible online for students. As a result, students usually do not have to visit libraries to complete traditional research. Many of the books students need are scanned and placed online.
• Questions for instructors can be asked over the phone, through an e-mail, or in a chat room. Instant messaging technology is becoming a very popular way for students and teachers to interact.

Working professionals, stay at home moms, and other people unable to attend college on a campus are utilizing distance learning programs to acquire more education or job training.

Choosing a Distance Learning School

Distance learning has existed for centuries through traditional mail and other creative means, but online education is still a relatively new field. Even though the technology is different, the educational mission and academic standards are the same as in traditional education: providing a quality education. Many online schools do exactly that, while others are merely degree mills or outright frauds. And of course, there’s plenty of variety in the middle.

As you search for online schools, it’s important to know what you’re looking for, and what to look out for. Below are some parameters to help you choose the best online school for you.

Accreditation

Accreditation is the first and most important aspect of any school. Make sure a proper association-preferably a regional association-has accredited the school you’re investigating. This will ensure that it is meeting academic requirements and that other institutions will accept the credits you earn. Attending a school that isn’t properly accredited will not only cause you headaches in college, but with future employers as well.

You also want to check that the accrediting agency is legit. Some of them don’t review schools very thoroughly and some will approve almost anyone for a fee. Your best bet is to verify that the accrediting association is endorsed by the U.S. Department of Education.

Quality of Faculty

By its nature, distance education is more independent than attending a physical classroom with other students, but having good teachers is still vital. Go online and research the faculty of any school you’re looking at. How much education and experience do the teachers have?

Just because a school delivers classes online doesn’t mean the teachers should be any less qualified. Most community college teachers have at least a Masters degree in their field. University professors typically have PhD’s. Any good online school should have comparable faculty.

Degree Programs

As with any school, you need to research the academic programs at an online school and make sure they line up with your goals for higher education. These days, there are many options to choose from in distance education, so shop around and compare specific programs at different schools.

Does this school offer Associates degrees or professional certification? If so, are they recognized by other institutions of higher education? This is vital, especially if you plan on continuing on to a four-year university.

Look at the specific courses that are required for a degree. Do they look interesting, challenging, or make sense with the field of study? Are these classes that you would be attracted to? How do they compare to the course requirements at other online schools?

Understand the Requirements and Schedule

It is also wise to understand the academic requirements and the class schedule before you commit to an online program. In distance education, these factors can vary widely from program to program. Some courses allow you to work at your own pace while others have deadlines, a schedule of live virtual classes, or in-person testing administered by the instructor or a proctor.

Ask yourself a few questions about you as a student. Are you good at self-motivation and staying on task, or do you need structure and/or deadlines? Are you an auditory learner, meaning you can remember details better if you hear them, or a visual learner who can easily read and absorb information? Some online courses are filled with live or recorded video lectures, podcasts and multimedia lessons, while others rely mainly on written text.

Some online programs are hybrid and require some physical attendance, either for lectures or exams. Understand the schedule so you can be certain you can meet these requirements. Also make sure the school is not too far away from where you live.

How much is tuition and are there any hidden fees or extra costs? Distance education is a dynamic and competitive market, and cost and quality don’t always match up. If you’ve begun your search with one school in particular, expand it by looking at other schools with comparable tuition. Compare the quality and variety of degrees, experience of the teachers and feedback from former and current students.

Online college should typically cost a little less than attending a traditional college or university. If you’re looking at a particular online school, do a cost comparison with other colleges in your area.

Student Enrollment, Class Size and Office Hours

How many students does this school enroll? In general, more is better. If an established school isn’t attracting that many students, it’s probably not a good sign. The same goes for how long the school has been in business. The longer, the better. You still want to investigate the quality of their programs and faculty, but experience is usually a good sign.

Look beyond the enrollment numbers. What is the graduation rate? You want to attend a school that has a good record of students’ academic success. What is the student/teacher ratio and average class size? The class is online, but to succeed you will still need some individual attention from the instructor.

That should extend beyond the classroom. It’s often overlooked in online education, but students still require extra interaction with instructors, as well as access to tutoring and mentorship opportunities . Many long-distance teachers will keep online “office hours,” allowing students the same access to instructors as in traditional classes.

If possible, talk to some current or former students to get an idea of the availability of these important aspects of school.

Hardware, Software and Textbooks

Most online schools have basic requirements when it comes to computers. You won’t need a supercomputer, just something that is relatively up-to-date and able to handle word processing and typical online activity. In the case of some specialized courses, you’ll need expensive hardware, but not for most classes.

Software will usually be provided free of charge. In some cases you will need to buy software for the class, which can vary widely in price. The same goes for textbooks. Make sure you know the cost estimate for software and textbooks. They can add up fast.

Remember, distance education has a different delivery method than traditional education, but the goal remains the same. So do the academic standards. Use the same criteria you would for a physical college, just in a different context. If an online school is right for you, it should stand up to the test.

Choosing a Distance Learning Program

Since more students are enrolling in distance learning programs, those considering enrolling in one should be cautious when selecting a program. Not all programs offer high quality education. Many institutions are cashing in on this demand for distance learning by offering programs that are sub-par or non-accredited. Therefore, those looking for a distance learning program should conduct thorough research before selecting one.

Formats Vary Widely…and So Do Costs

For starters, we’re using the term “online course” to mean a college course offered for credit that can be taken completely online. But within that narrow definition exist many different types of courses. Some are little more than correspondence courses: pre-recorded videos with minimal personal involvement from the professor. Some (like Ohio State’s online bachelors program) are videos recorded live and archived for later viewing. Others go far beyond video, offering richly interactive learning materials and robust professor interactions.

Costs also vary considerably. Many of the high-quality online programs are offered through traditional universities, where the online courses are just as expensive as their on-campus counterparts. Devon Haynie at US News found that most online courses from traditional 4-year programs cost $300-400 per credit hour, plus additional fees. She signed up for a personal finance course that cost $1300 total. There were discounts to be had for in-state tuition, and she found a community college option for $515.

Of course, a quick Google search reveals many low- and no-cost options. But beware: many of these are from unaccredited schools or can’t be taken for college credit. As a rule of thumb, if something is worth $1300 from a well-known school, you should view with suspicion the $25 version from a school you’ve never heard of.

Bottom line: expect to pay a decent amount for decent quality online education, and understand that courses will vary in format and quality.

Know What You’re Paying For

Along the same lines, it’s important to know what you’re paying for before you buy. If you’re enrolling in an entirely online program, this comes into play before you enroll. Do your research. US News offers an independent ranking of entirely online programs, with in-depth reviews of the top programs.

If on the other hand you’re enrolling in a few courses here and there (perhaps to finish out a degree you’ve already started), you need to do your research for each course. Say you paid $1000 for a robust, high-quality course and were happy with your investment. Will you be happy to drop another $1000 on a lesser quality course? Make sure you know what you’re getting before you buy.

Know What’s Expected of You

Online courses are marketed to busy professionals and adults trying to complete degrees. Marketers emphasize the freedom and convenience, but these courses are still a ton of work. Before you enroll in and pay for a course, find out what kind of time commitment you’re making. Many college courses, online or not, require 15 hours or more per week. Some have huge projects that spike the needed time drastically upward one or two weeks of the semester.

Bottom line: take the time to find out what will be expected of you, then evaluate whether you are able to give that kind of time—before you buy.

Have a Plan

If your ultimate goal is a degree, then taking an online class here or there because the content sounds interesting or useful isn’t a real plan. Instead, you need to create a plan that leads to your goal.

You may be part-way through a residential degree program, looking to fill some course gaps. You may be working on getting an academic head start by knocking out some early courses online before heading to a residential program. Or you may be looking for a degree that can be earned entirely online. Whatever your approach, be sure to formulate a plan that leads to a degree before enrolling in an online course.

The good news is you shouldn’t need to go it alone. You should have access to a faculty advisor or coach who can help you craft this plan. If your prospective online college can’t offer you this kind of guidance, consider that a red flag.

Understand Some Potential Downsides

Online programs can leave students feeling a bit isolated. Face-to-face (two-way) video interaction with instructors is rare to nonexistent, and collaboration with classmates is often limited to chat and email. The collaboration and camaraderie residential students enjoy just isn’t there. Look for programs that work hard to overcome this, but understand that it’s an issue even in the best programs.

Online programs have a lower graduation rate than residential or blended (part residential, part online) programs, notes James Paterson at EducationDive. Experts disagree on the reasons why, but it may have to do with the greater level of self-motivation and direction that’s needed to succeed while feeling all on your own.

To offset these downsides, look for quality online programs with robust student services and student support. The old adage “you get what you pay for” is generally true here: the cheapest courses have the lowest levels of student support.

Know Your Way Around a Computer

You don’t have to be a technology expert to make online college work, but you do need to be conversant. Every school has its own learning management system, or LMS. You may need to troubleshoot why video isn’t playing (do you have the necessary software/codec/browser?). Courses will have their own platforms and systems and even sometimes specialized software. And of course, nothing works without a stable internet connection.

Bottom line: if the lingo in the previous paragraph scares you, you may need to brush up on your computer skills before diving into online education.

Realize That Convenient Doesn’t Mean Easy

Online classes and degree programs offer a measure of convenience that a traditional residential program can’t match. The ability to watch lectures from anywhere and on your own timetable is indispensable for some. But don’t make the mistake of thinking that an online program will be easier than an in-person one. Quality online programs are just as rigorous as their brick-and-mortar counterparts.

In fact, some students may find online programs more difficult than residential programs. The content is the same, but online programs lack some of the accountability and presence of traditional programs. In a traditional program, students have to show up at a set time 2 or 3 times a week. And when they do miss class, those students have to face the teacher’s disapproving gaze the next class period. It’s much easier to fall behind when you’re left to set your own schedule. And it’s easier to let things slide when you don’t feel the in-person pressure from the professor.

Ensure Your Credits Will Transfer Before Enrolling

If you have any college credit to your name already, be sure your existing college credit and your prospective online education will play nice. The goal is to graduate from one school or the other, but not every school accepts credits from every other school. And some schools may accept some credits but not others from another school. Whether you intend to earn your degree from the online or the traditional university, be certain that all your needed credits will transfer. Do this before enrolling online.

MOOCs Are No Substitute for Traditional Online Courses

Massive Open Online Courses (MOOCs) are trendy. You can learn from professors at elite universities, for free! And as a general enrichment tool, they are pretty cool. But make no mistake: MOOCs are no substitute for more conventional (and less free) online courses. They have a low completion rate and usually have little accountability or assessment. The vast majority of MOOCs don’t offer college credits, either. Class Central reports on a few ways to take MOOCs for credit, but the primary way of doing so turns the free course into a $649 course. With that kind of price tag, why not go the conventional route and benefit from interaction with your professor?

Bottom line: take these for fun or for general enrichment. But if you’re looking to earn a degree, these almost certainly aren’t the right choice for you.

Look for Accreditation

Schools offering online degree programs can be accredited just like traditional colleges and universities. Accreditation is a kind of seal of approval granted by an independent accrediting organization. These organizations evaluate a school’s quality and verify whether the school complies with education law.

Some employers and many graduate schools require an accredited degree for either hire or admission. Don’t shortchange yourself by spending time and effort to earn a degree from an unaccredited school. It’s not worth the risk.

Student Success

You want your diploma to be the key to future opportunities, not just a piece of paper. Other than accreditation, how can you better know what your degree can accomplish for you?

Start by looking at student success. Quality programs will advertise their student successes. They may advertise high boards passing percentages in a nursing program, above-average med school acceptance rates, or a percentage of prospective graduates hired before graduation. These statistics don’t guarantee you your dream job, of course, but they do suggest which programs and schools are high in quality.

Conversely, investigate graduation/completion rates and rate of student loan defaults. Low graduation rates and high default rates suggest a program is not serving its students well. According to a Chicago Tribune story by Maria Danilova, for-profit colleges are the worst offenders by both these metrics.

Graduation rates are easily obtainable, and the higher, the better. Remember, though, that online program graduation rates overall will be lower than residential programs. The rate of student loan defaults can be trickier to track down, as schools aren’t required to divulge this. But this is the internet: if a school gets a bad reputation in this area, it won’t be too hard to find out.

What Makes a Good Online Course?

Not all online classes are created equal. Unfortunately, there’s a whole heap of shoddily produced, bore-you-to-tears classes out there that, in the end, just don’t provide quality education. On the other hand, there are some truly inspired, engaging, and educational classes that really live up to the potential of distance learning. Here are a few key factors that make all the difference to the quality of online courses:

1. Proper Pacing

It’s a fine balance to strike, but the best online courses are the ones which are properly paced. Proper pacing means the student is neither bored nor overwhelmed; they have plenty of time–and notice–to complete large projects, but also are kept engaged by small assignments in the interim. These small assignments should never stack up and bury the student in stress and anxiety, nor should they be pointless, tedious busy work.

2. Multimedia Integration

The really exciting part of online classes is the fact that they can present content in ways that books and lectures can’t. Great online courses take advantage of this fact, and incorporate various multimedia elements into the presentation, such as videos, podcasts, interactive activities, and more. This is certainly more engaging than reading a long text document, and the information is much more likely to be retained. It’s not enough, however, to simply add in multimedia for multimedia’s sake. The content must be done well, and with a clear purpose. A forty-minute video of the professor mumbling into his webcam does not qualify as good use of multimedia.

3. Quality Content

When a course’s content is of high quality, you find yourself engaged and curious, and as a result you learn much more naturally. This is in stark contrast to those courses which only use dismal, bland textbooks and regurgitative, fill-in-the-blank quizzes. Low quality content feels like a chore, and the information is seldom retained. High quality content can be of any modality: videos, websites, audio presentations, etc. What’s important is this: does it lend itself to natural learning? Would an expert in the field recommend it to anyone curious about the subject, regardless of the online class? This is the sort of content used by the best online courses.

4. Self-Directed Learning

The best online classes recognize that their students are adults who have the ability to make up their own mind and take responsibility for their own education. When the course is too micromanaged, when the assignments are dictated to the smallest detail, students become frustrated or, at best, don’t make any meaningful discoveries on their own. Good online courses give students the freedom to design their own projects and explore the aspects of the subject which are most interesting to them.

5. Community Connection

One of the biggest risks that online courses face is a sense of student isolation. Great online courses combat this risk by encouraging online interaction between students and faculty. For example, a class may have an off-topic discussion board, where students can feel free to chat about anything that interests them–the playoffs, for example, or a tasty new recipe. Or a class may require students to work on a group project together via an online forum. This fosters a sense of community, and gives students the support needed to ask questions or seek guidance.

6. Multiple Learning Modalities

Everyone learns differently. Some students are very visually oriented; others need to hear information out loud to retain it. The best online courses integrate as many learning modalities as possible–visual, auditory, kinesthetic, musical, and so on–into the presentation. This way, students are able to study in the way that works best for them.

7. Intuitive Navigation

The layout of the course should be clear and easy to follow. Students should always know what to do next, and should always know how to access relevant information and resources. The best courses have been reviewed by third party organizations and are designed to be intuitive to navigate.

8. Reliable Technology

Many courses, in an attempt to be flashy or stylish, utilize a host of technologies in their presentation, often requiring students to download a dozen new plug-ins or sign up for outside services. The problem with this is that it doesn’t always work, and everyone wastes a boatload of time and energy troubleshooting. The best courses use only technologies which are as reliable and as universally supported as possible. This makes the online learning experience much more pleasant for everyone involved.

9. Room for Additional Exploration

Great online courses provide curious students with resources which provide additional information and a greater depth of detail. It’s another fine balance: having too many supplemental add-ons can be confusing or stressful, but it’s important to give students an opportunity to learn more if they wish to do so. The key is to clearly differentiate the core class requirements from the additional resources, so students know exactly what’s expected of them, and what options are available.

10. Creative Design

It’s a hard quality to define, but the best courses are designed to give students a varied and fresh learning experience week after week. All too often, online courses fall into a formula, and repeat that formula over and over for the entire duration of the class. This will be a very dull experience for the students, and the actual educational value of the course will suffer as well. The best courses are designed by people who put careful thought and focused effort towards creating a unique and engaging class experience, from start to finish.

Similar Posts:

• Discover Your Learning Style – Comprehensive Guide on Different Learning Styles
• Guide on College and University Admissions
• 35 of the BEST Educational Apps for Teachers (Updated 2024)

Leave a Comment Cancel reply

Save my name and email in this browser for the next time I comment.

Guidance on distance learning

School closures were mandated as part of public health efforts to contain the spread of COVID-19 from February 2020 in most countries. Education systems around the world are facing an unprecedented challenge. Governmental agencies are working with international organizations, private sector partners and civil society to deliver education remotely through a mix of technologies in order to ensure continuity of curriculum-based study and learning for all.

Supporting distance learning during COVID-19

UNESCO has been working to mitigate the impact of education disruption and school closures. In response to the pandemic, UNESCO has produced various distance learning resources to support teachers and policy-makers. The following resources offer best practices, innovative ideas and practical information.

UNESCO is supporting the organization of several workshops based on the publication Ensuring effective distance learning during COVID-19 disruption: guidance for teachers following a call for proposals that attracted almost 200 entries. The first workshop was held online on 20-21 October 2021, sponsored by the Faculty of Specific Education at Alexandria University in Egypt. Organized by Prof. Mona Sharaf Abdelgalil, the session was attended by 211 teachers from different educational departments, including 80% female teachers. The second set of workshops (six in-person, and one hybrid) took place in Zimbabwe from 5 to 24 November 2021, organized by Learning Factory and Mr Addi Mavengere. These workshops reached a total of 95 participants, including 67% female teachers, 64% from rural communities, and 1% with physical impairment. Six other pilot workshops were held in-person in Ethiopia by Mr Inku Fasil, targeting 120 teachers in Bahirdar, Addis Ababa and Adama from 13 November to 2 December 2021.

Case studies

10 recommendations to plan distance learning solutions.

Decide on the use high-technology and low-technology solutions based on the reliability of local power supplies, internet connectivity, and digital skills of teachers and students. This could range through integrated digital learning platforms, video lessons, MOOCs, to broadcasting through radios and TVs.

Implement measures to ensure that students including those with disabilities or from low-income backgrounds have access to distance learning programmes, if only a limited number of them have access to digital devices. Consider temporarily decentralizing such devices from computer labs to families and support them with internet connectivity.

Assess data security when uploading data or educational resources to web spaces, as well as when sharing them with other organizations or individuals. Ensure that the use of applications and platforms does not violate students’ data privacy.

Mobilize available tools to connect schools, parents, teachers and students with each other. Create communities to ensure regular human interactions, enable social caring measures, and address possible psychosocial challenges that students may face when they are isolated.

Organize discussions with stakeholders to examine the possible duration of school closures and decide whether the distance learning programme should focus on teaching new knowledge or enhance students’ knowledge of prior lessons. Plan the schedule depending on the situation of the affected zones, level of studies, needs of students needs, and availability of parents. Choose the appropriate learning methodologies based on the status of school closures and home-based quarantines. Avoid learning methodologies that require face-to-face communication.

Organize brief training or orientation sessions for teachers and parents as well, if monitoring and facilitation are needed. Help teachers to prepare the basic settings such as solutions to the use of internet data if they are required to provide live streaming of lessons.

Blend tools or media that are available for most students, both for synchronous communication and lessons, and for asynchronous learning. Avoid overloading students and parents by asking them to download and test too many applications or platforms.

Define the rules with parents and students on distance learning. Design formative questions, tests, or exercises to monitor closely students’ learning process. Try to use tools to support submission of students’ feedback and avoid overloading parents by requesting them to scan and send students’ feedback.

Keep a coherent timing according to the level of the students’ self-regulation and metacognitive abilities especially for livestreaming classes. Preferably, the unit for primary school students should not be more than 20 minutes, and no longer than 40 minutes for secondary school students. 

Create communities of teachers, parents and school managers to address sense of loneliness or helplessness, facilitate sharing of experience and discussion on coping strategies when facing learning difficulties.

National distance learning platforms and tools

A collection of national learning platforms and tools from Member States to facilitate the search for resources in one place.

Related items

• Information and communication

• To save this word, you'll need to log in. Log In

distance learning

Definition of distance learning

Examples of distance learning in a sentence.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'distance learning.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Dictionary Entries Near distance learning

distance language

distanceless

Cite this Entry

“Distance learning.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/distance%20learning. Accessed 14 May. 2024.

Subscribe to America's largest dictionary and get thousands more definitions and advanced search—ad free!

Can you solve 4 words at once?

Word of the day.

See Definitions and Examples »

Get Word of the Day daily email!

Popular in Grammar & Usage

More commonly misspelled words, your vs. you're: how to use them correctly, every letter is silent, sometimes: a-z list of examples, more commonly mispronounced words, how to use em dashes (—), en dashes (–) , and hyphens (-), popular in wordplay, the words of the week - may 10, a great big list of bread words, 10 scrabble words without any vowels, 8 uncommon words related to love, 9 superb owl words, games & quizzes.

• Subject List
• Take a Tour
• For Authors
• Subscriber Services
• Publications
• African American Studies
• African Studies
• American Literature
• Anthropology
• Architecture Planning and Preservation
• Art History
• Atlantic History
• Biblical Studies
• British and Irish Literature
• Childhood Studies
• Chinese Studies
• Cinema and Media Studies
• Communication
• Criminology
• Environmental Science
• Evolutionary Biology
• International Law
• International Relations
• Islamic Studies
• Jewish Studies
• Latin American Studies
• Latino Studies
• Linguistics
• Literary and Critical Theory
• Medieval Studies
• Military History
• Political Science
• Public Health
• Renaissance and Reformation
• Social Work
• Urban Studies
• Victorian Literature
• Browse All Subjects

How to Subscribe

• Free Trials

In This Article Expand or collapse the "in this article" section Distance Learning

Introduction, defining distance learning.

• Distance Learning Organizations
• Distance Learning Journals
• History and Pioneers of Distance Learning
• Foundations of Distance Learning
• Teaching at a Distance
• Quality and Effectiveness of Distance Learning
• K–12 Online Learning
• Current and Emerging Trends

Related Articles Expand or collapse the "related articles" section about

About related articles close popup.

Lorem Ipsum Sit Dolor Amet

Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Aliquam ligula odio, euismod ut aliquam et, vestibulum nec risus. Nulla viverra, arcu et iaculis consequat, justo diam ornare tellus, semper ultrices tellus nunc eu tellus.

• Blended Learning
• Educational Technology
• Online Education

Other Subject Areas

Forthcoming articles expand or collapse the "forthcoming articles" section.

• English as an International Language for Academic Publishing
• Girls' Education in the Developing World
• History of Education in Europe
• Find more forthcoming articles...
• Export Citations
• Share This Facebook LinkedIn Twitter

Distance Learning by Patrick Lowenthal , Kerry Rice , Sarah Rich , Shelly Walters LAST REVIEWED: 30 April 2021 LAST MODIFIED: 26 July 2017 DOI: 10.1093/obo/9780199756810-0186

Distance learning, also referred to as “distance education” and sometimes simply as “online learning” or “distributed learning,” is a term used to describe the practice of learning at a distance. Historically, distance learning dates back to the 1880s and was defined by a teacher and a student being separated by space and time. This early form of distance learning is often described as correspondence education, where a student might complete lessons from a workbook and then mail them to a teacher. Over the years, however, distance learning changed along with technology. For instance, with advances in technology, teachers and students were able to interact in more sophisticated ways while still being separated by space and time. Although this separation of student and teacher by space and time is still a hallmark of distance learning, there are now many variations of distance learning—ranging from self-paced “correspondence”-like courses, asynchronous group-paced online courses, and informal synchronous (e.g., webinars) and asynchronous videos (e.g., Kahn Academy), to name a few. From its inception, distance learning has attracted critics who are skeptical of whether one can learn effectively at a distance. However, distance learning continues to grow. In the 21st century, asynchronous group-paced online learning is the most popular form of distance learning and many estimate that one in five college students take at least one online course each year; however, there are also growing numbers of students completing some form of blended learning that leverages some aspect of distance learning in face-to-face courses and in turn continues to blur the lines between “distance learning” and “face-to-face” learning. The following sections will focus on distance learning in general but also specifically on how online learning (the most popular form of distance learning) is changing when and how people learn.

As distance learning grows in popularity, practitioners and researchers alike struggle to define distance learning. They also struggle to agree on what the most important defining features of distance are. For instance, is the most defining feature the “distance” between teacher and student, the separation in time, or the technology used ( Graham 2006 )? The rise of the Internet and specifically online learning has only complicated this by introducing countless variations and hybrid or blended forms of distance learning. In the following articles, chapters, and books, the authors specifically address this issue of defining distance learning. For instance, Keegan 1980 analyzes four early definitions of distance education, which Garrison and Shale 1987 later respond to by offering what they see as a less restrictive definition; Barker, et al. 1989 in turn critiques Garrison and Shale’s definition as not differentiating between correspondence- and telecommunications-based distance education. Schlosser and Simonson 2009 tries to build upon this earlier research by offering an updated definition of distance learning. Graham 2006 describes trends of blending distance learning into traditional face-to-face courses in the form of blended learning. But as the complexity of the distance learning landscape continues to grow and change, Harasim 2006 ; Lowenthal, et al. 2009 ; and Moore, et al. 2011 all try to describe the different ways that distance learning is understood and practiced. Finally, Cavanaugh, et al. 2009 provides a K–12 perspective of how distance learning is defined and ultimately practiced.

Barker, B. O., A. G. Frisbie, and K. R. Patrick. 1989. Concepts: Broadening the definition of distance education in light of the new telecommunications technologies. American Journal of Distance Education 3.1: 20–29.

DOI: 10.1080/08923648909526647

Building on the previous work of Keegan and Garrison and Shale, this article critiques previous definitions as not being broad enough and the conception of distance education as too narrow within correspondence studies. They argue that a definition of distance education must differentiate between correspondence-based distance education and what they call “telecommunications-based” distance education.

Cavanaugh, C., M. Barbour, and R. Brown, et al. 2009. Research committee issues brief: Examining communication and interaction in online teaching . Vienna: International Association for K-12 Online Learning.

Written by a number of leaders in K–12 online learning, this brief focuses on identifying key aspects of online teaching. Provides a nice overview of different elements of online teaching, which is important to consider when defining distance learning.

Garrison, D. R., and D. Shale. 1987. Mapping the boundaries of distance education: Problems in defining the field. American Journal of Distance Education 1.1: 7–13.

DOI: 10.1080/08923648709526567

Garrison and Shale analyze Keegan’s previous attempts at defining distance education and argue for a less restrictive definition that takes into account advances in communications technology.

Graham, C. R. 2006. Blended learning systems: Definition, current trends, and future directions. In Handbook of blended learning: Global perspectives, local designs . Edited by C. J. Bonk and C. R. Graham, 3–21. San Francisco: Pfeiffer.

Graham defines blended learning and describes current trends and future directions in this foundational chapter on blended learning.

Harasim, L. 2006. A history of e-learning: Shift happened. In The international handbook of virtual learning environments . Edited by J. Weiss, J. Nolan, J. Hunsinger, and P. Trifonas, 59–94. Netherlands: Springer.

DOI: 10.1007/978-1-4020-3803-7_2

In this chapter, Harasim (a pioneer of online learning) presents a brief history of online learning. She highlights a paradigm shift that has taken place that focuses more on networked learning than independent learning.

Keegan, D. J. 1980. On defining distance education. Distance Education 1.1: 13–36.

DOI: 10.1080/0158791800010102

In this foundational and often-cited article, Keegan analyzes four popular early definitions of distance learning to create a “comprehensive definition.” These definitions and the themes Keegan identifies still have relevance in the 21st century.

Lowenthal, P. R., B. Wilson, and P. Parrish. 2009. Context matters: A description and typology of the online learning landscape . In 32nd Annual proceedings: Selected research and development papers presented at the annual convention of the Association for Educational Communications and Technology . Washington, DC: AECT.

This paper argues that online learning is diverse and manifests in different ways across different contexts. For instance, online learning often looks different in K–12, higher education, and corporate/industry. They develop a typology that can be used for instructional designers as well as researchers to better describe the type of online learning they are interested in studying or developing.

Moore, J. L., C. Dickson-Deane, and K. Galyen. 2011. E-learning, online learning, and distance learning environments: Are they the same? Internet and Higher Education 14.2: 129–135.

DOI: 10.1016/j.iheduc.2010.10.001

Investigates the way people use terms such as e-learning, online learning, and distance learning. They found that while people often like to think of these terms as being synonymous, people in fact think differently about each of them.

Schlosser, L., and M. Simonson. 2009. Distance education: Definition and glossary of terms . 3d ed. Charlotte, NC: Information Age.

Offers a recent and commonly accepted definition that also addresses previous research and theories of distance education. The book also includes a glossary of common terms related to distance education.

back to top

Users without a subscription are not able to see the full content on this page. Please subscribe or login .

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here .

• About Education »
• Meet the Editorial Board »
• Academic Achievement
• Academic Audit for Universities
• Academic Freedom and Tenure in the United States
• Action Research in Education
• Adjuncts in Higher Education in the United States
• Administrator Preparation
• Adolescence
• Advanced Placement and International Baccalaureate Courses
• Advocacy and Activism in Early Childhood
• African American Racial Identity and Learning
• Alaska Native Education
• Alternative Certification Programs for Educators
• Alternative Schools
• American Indian Education
• Animals in Environmental Education
• Art Education
• Artificial Intelligence and Learning
• Assessing School Leader Effectiveness
• Assessment, Behavioral
• Assessment, Educational
• Assessment in Early Childhood Education
• Assistive Technology
• Augmented Reality in Education
• Beginning-Teacher Induction
• Bilingual Education and Bilingualism
• Black Undergraduate Women: Critical Race and Gender Perspe...
• Case Study in Education Research
• Changing Professional and Academic Identities
• Character Education
• Children’s and Young Adult Literature
• Children's Beliefs about Intelligence
• Children's Rights in Early Childhood Education
• Citizenship Education
• Civic and Social Engagement of Higher Education
• Classroom Learning Environments: Assessing and Investigati...
• Classroom Management
• Coherent Instructional Systems at the School and School Sy...
• College Admissions in the United States
• College Athletics in the United States
• Community Relations
• Comparative Education
• Computer-Assisted Language Learning
• Computer-Based Testing
• Conceptualizing, Measuring, and Evaluating Improvement Net...
• Continuous Improvement and "High Leverage" Educational Pro...
• Counseling in Schools
• Critical Approaches to Gender in Higher Education
• Critical Perspectives on Educational Innovation and Improv...
• Critical Race Theory
• Crossborder and Transnational Higher Education
• Cross-National Research on Continuous Improvement
• Cross-Sector Research on Continuous Learning and Improveme...
• Cultural Diversity in Early Childhood Education
• Culturally Responsive Leadership
• Culturally Responsive Pedagogies
• Culturally Responsive Teacher Education in the United Stat...
• Curriculum Design
• Data Collection in Educational Research
• Data-driven Decision Making in the United States
• Deaf Education
• Desegregation and Integration
• Design Thinking and the Learning Sciences: Theoretical, Pr...
• Development, Moral
• Dialogic Pedagogy
• Digital Age Teacher, The
• Digital Citizenship
• Digital Divides
• Disabilities
• Distance Learning
• Distributed Leadership
• Doctoral Education and Training
• Early Childhood Education and Care (ECEC) in Denmark
• Early Childhood Education and Development in Mexico
• Early Childhood Education in Aotearoa New Zealand
• Early Childhood Education in Australia
• Early Childhood Education in China
• Early Childhood Education in Europe
• Early Childhood Education in Sub-Saharan Africa
• Early Childhood Education in Sweden
• Early Childhood Education Pedagogy
• Early Childhood Education Policy
• Early Childhood Education, The Arts in
• Early Childhood Mathematics
• Early Childhood Science
• Early Childhood Teacher Education
• Early Childhood Teachers in Aotearoa New Zealand
• Early Years Professionalism and Professionalization Polici...
• Economics of Education
• Education For Children with Autism
• Education for Sustainable Development
• Education Leadership, Empirical Perspectives in
• Education of Native Hawaiian Students
• Education Reform and School Change
• Educational Statistics for Longitudinal Research
• Educator Partnerships with Parents and Families with a Foc...
• Emotional and Affective Issues in Environmental and Sustai...
• Emotional and Behavioral Disorders
• Environmental and Science Education: Overlaps and Issues
• Environmental Education
• Environmental Education in Brazil
• Epistemic Beliefs
• Equity and Improvement: Engaging Communities in Educationa...
• Equity, Ethnicity, Diversity, and Excellence in Education
• Ethical Research with Young Children
• Ethics and Education
• Ethics of Teaching
• Ethnic Studies
• Evidence-Based Communication Assessment and Intervention
• Family and Community Partnerships in Education
• Family Day Care
• Federal Government Programs and Issues
• Feminization of Labor in Academia
• Finance, Education
• Financial Aid
• Formative Assessment
• Future-Focused Education
• Gender and Achievement
• Gender and Alternative Education
• Gender, Power and Politics in the Academy
• Gender-Based Violence on University Campuses
• Gifted Education
• Global Mindedness and Global Citizenship Education
• Global University Rankings
• Governance, Education
• Grounded Theory
• Growth of Effective Mental Health Services in Schools in t...
• Higher Education and Globalization
• Higher Education and the Developing World
• Higher Education Faculty Characteristics and Trends in the...
• Higher Education Finance
• Higher Education Governance
• Higher Education Graduate Outcomes and Destinations
• Higher Education in Africa
• Higher Education in China
• Higher Education in Latin America
• Higher Education in the United States, Historical Evolutio...
• Higher Education, International Issues in
• Higher Education Management
• Higher Education Policy
• Higher Education Research
• Higher Education Student Assessment
• High-stakes Testing
• History of Early Childhood Education in the United States
• History of Education in the United States
• History of Technology Integration in Education
• Homeschooling
• Inclusion in Early Childhood: Difference, Disability, and ...
• Inclusive Education
• Indigenous Education in a Global Context
• Indigenous Learning Environments
• Indigenous Students in Higher Education in the United Stat...
• Infant and Toddler Pedagogy
• Inservice Teacher Education
• Integrating Art across the Curriculum
• Intelligence
• Intensive Interventions for Children and Adolescents with ...
• International Perspectives on Academic Freedom
• Intersectionality and Education
• Knowledge Development in Early Childhood
• Leadership Development, Coaching and Feedback for
• Leadership in Early Childhood Education
• Leadership Training with an Emphasis on the United States
• Learning Analytics in Higher Education
• Learning Difficulties
• Learning, Lifelong
• Learning, Multimedia
• Learning Strategies
• Legal Matters and Education Law
• LGBT Youth in Schools
• Linguistic Diversity
• Linguistically Inclusive Pedagogy
• Literacy Development and Language Acquisition
• Literature Reviews
• Mathematics Identity
• Mathematics Instruction and Interventions for Students wit...
• Mathematics Teacher Education
• Measurement for Improvement in Education
• Measurement in Education in the United States
• Meta-Analysis and Research Synthesis in Education
• Methodological Approaches for Impact Evaluation in Educati...
• Methodologies for Conducting Education Research
• Mindfulness, Learning, and Education
• Mixed Methods Research
• Motherscholars
• Multiliteracies in Early Childhood Education
• Multiple Documents Literacy: Theory, Research, and Applica...
• Multivariate Research Methodology
• Museums, Education, and Curriculum
• Music Education
• Narrative Research in Education
• Native American Studies
• Nonformal and Informal Environmental Education
• Note-Taking
• Numeracy Education
• One-to-One Technology in the K-12 Classroom
• Open Education
• Organizing for Continuous Improvement in Education
• Organizing Schools for the Inclusion of Students with Disa...
• Outdoor Play and Learning
• Outdoor Play and Learning in Early Childhood Education
• Pedagogical Leadership
• Pedagogy of Teacher Education, A
• Performance Objectives and Measurement
• Performance-based Research Assessment in Higher Education
• Performance-based Research Funding
• Phenomenology in Educational Research
• Philosophy of Education
• Physical Education
• Podcasts in Education
• Policy Context of United States Educational Innovation and...
• Politics of Education
• Portable Technology Use in Special Education Programs and ...
• Post-humanism and Environmental Education
• Pre-Service Teacher Education
• Problem Solving
• Productivity and Higher Education
• Professional Development
• Professional Learning Communities
• Program Evaluation
• Programs and Services for Students with Emotional or Behav...
• Psychology Learning and Teaching
• Psychometric Issues in the Assessment of English Language ...
• Qualitative Data Analysis Techniques
• Qualitative, Quantitative, and Mixed Methods Research Samp...
• Qualitative Research Design
• Quantitative Research Designs in Educational Research
• Queering the English Language Arts (ELA) Writing Classroom
• Race and Affirmative Action in Higher Education
• Reading Education
• Refugee and New Immigrant Learners
• Relational and Developmental Trauma and Schools
• Relational Pedagogies in Early Childhood Education
• Reliability in Educational Assessments
• Religion in Elementary and Secondary Education in the Unit...
• Researcher Development and Skills Training within the Cont...
• Research-Practice Partnerships in Education within the Uni...
• Response to Intervention
• Restorative Practices
• Risky Play in Early Childhood Education
• Scale and Sustainability of Education Innovation and Impro...
• Scaling Up Research-based Educational Practices
• School Accreditation
• School Choice
• School Culture
• School District Budgeting and Financial Management in the ...
• School Improvement through Inclusive Education
• School Reform
• Schools, Private and Independent
• School-Wide Positive Behavior Support
• Science Education
• Secondary to Postsecondary Transition Issues
• Self-Regulated Learning
• Self-Study of Teacher Education Practices
• Service-Learning
• Severe Disabilities
• Single Salary Schedule
• Single-sex Education
• Single-Subject Research Design
• Social Context of Education
• Social Justice
• Social Network Analysis
• Social Pedagogy
• Social Science and Education Research
• Social Studies Education
• Sociology of Education
• Standards-Based Education
• Statistical Assumptions
• Student Access, Equity, and Diversity in Higher Education
• Student Assignment Policy
• Student Engagement in Tertiary Education
• Student Learning, Development, Engagement, and Motivation ...
• Student Participation
• Student Voice in Teacher Development
• Sustainability Education in Early Childhood Education
• Sustainability in Early Childhood Education
• Sustainability in Higher Education
• Teacher Beliefs and Epistemologies
• Teacher Collaboration in School Improvement
• Teacher Evaluation and Teacher Effectiveness
• Teacher Preparation
• Teacher Training and Development
• Teacher Unions and Associations
• Teacher-Student Relationships
• Teaching Critical Thinking
• Technologies, Teaching, and Learning in Higher Education
• Technology Education in Early Childhood
• Technology, Educational
• Technology-based Assessment
• The Bologna Process
• The Regulation of Standards in Higher Education
• Theories of Educational Leadership
• Three Conceptions of Literacy: Media, Narrative, and Gamin...
• Tracking and Detracking
• Traditions of Quality Improvement in Education
• Transformative Learning
• Transitions in Early Childhood Education
• Tribally Controlled Colleges and Universities in the Unite...
• Understanding the Psycho-Social Dimensions of Schools and ...
• University Faculty Roles and Responsibilities in the Unite...
• Using Ethnography in Educational Research
• Value of Higher Education for Students and Other Stakehold...
• Virtual Learning Environments
• Vocational and Technical Education
• Wellness and Well-Being in Education
• Women's and Gender Studies
• Young Children and Spirituality
• Young Children's Learning Dispositions
• Young Children's Working Theories
• Privacy Policy
• Cookie Policy
• Legal Notice
• Accessibility

Powered by:

• [66.249.64.20|185.147.128.134]
• 185.147.128.134

Tips for Online Students , Tips for Students

What is Distance Learning? The Benefits of Studying Remotely

Updated: January 22, 2024

Published: March 18, 2020

What is distance learning? The distance learning definition may seem confusing at first, but it’s quite simple, and it may even be the right kind of education for you. Let’s learn more about distance education, how it’s different from online learning, and if it’s the ideal fit for you.

What Is Distance Learning?

Merriam Webster defines distance learning as, “a method of study where teachers and students do not meet in a classroom but use the Internet, e-mail, mail, etc., to have classes.”

Simply put, distance learning is when students are separated from teachers and peers. This means that students learn remotely and do not have face-to-face learning with instructors or other students.

Photo by  Bonnie Kittle  on  Unsplash

What’s the difference between online learning and distance learning, 1. location.

Online learning can include the use of online tools and platforms while still being in a regular classroom setting. Distance learning, however, is remote and does not include any face-to-face interaction between student and teacher.

2. Interaction

Online learning, as seen above, can include interaction with teachers and peers, whereas distance learning does not have in-person interactions.

3. Intention

Online learning can be used as a supplement for teachers in their courses, while distance learning replaces teachers with instruction that is pre-set on the learning platform.

What Is Online Learning?

Online learning is when teachers or students use educational tools which are accessible on the internet.

This means that students can also use online tools while they are physically in a classroom with their teacher and peers. Online learning can be used anywhere and anytime, so teachers may have students using them as tools in class or for preparation and assignments at home.

Online learning tools are often used to create blended learning environments in the classroom. This helps keep students engaged in the class and in the material.

Online learning also helps teachers save preparation time before class. With the help of online educational tools, teachers can spend more time grading papers, giving one-on-one attention to students, and maybe even getting some free time for themselves in their busy work schedule.

Photo by  John Schnobrich  on  Unsplash

What is distance education.

Distance learning does not include any in-person interaction with an instructor or study peers. Students study at home on their own, and the learning is more individual and varies on speed and timeline according to each individual student and their availability.

Distance learning actually relies on the educational tools of online learning, and that is probably why there is some confusion between the two. It is possible to study with online distance learning as well. In that sense, distance learning is a subset of online learning.

Because distance education is remote, it can connect students to universities worldwide, making it more accessible for students in different countries. It is also known to be more affordable, which is another factor that helps make education more accessible to many students around the world and in different socio-economic levels.Students from diverse backgrounds can unite in their pursuit of learning, armed with a wealth of available educational information from the Studocu online resource that will improve their academic path.

Photo by  Bench Accounting  on  Unsplash

The benefits of distance learning.

As mentioned above, students can study from universities around the world, even if they are not able to travel to their preferred program. This allows top universities to be available to students who would not otherwise be able to attend due to distance, finances, or other circumstances.

Distance learning is extremely important for those who cannot attend programs due to health complications, severe social anxiety, busy work schedules or parenting demands, or any other situations which make it necessary to be confined to the home.

Online programs, such as University of the People , cater to students who prefer or need distance education. UoPeople is a tuition-free nonprofit institution, making it an affordable and sustainable option for students worldwide. In addition to being affordable, University of the People employs academic leadership from renowned universities around the world, allowing equal opportunity for students to access quality education.

UoPeople provides distance education for students who may have physical or health restrictions, those who live in remote areas, or those who cannot otherwise attend school due to late work hours or raising a family. This provides an equal opportunity for people to access higher education despite restrictions or location.

Types Of Distance Learning

1. online courses.

Online courses are usually offered as additional classes in traditional degrees. As long as students have computer and internet access, they can learn and receive instruction at home.

2. Hybrid courses

Hybrid courses combine traditional classroom settings with online learning at home. This can mean that students learn individually at home and meet up for in-person instructions or lectures at certain intervals during the course. The amount of at-home learning and in-class learning varies for each hybrid course.

3. Conference classes

Conferencing allows students and teachers to meet up for class in real time, whether in a group or one-on-one with an instructor. Using the phone or video chatting, such as Skype, students and teachers can engage in live lessons despite distance.

4. Correspondence courses

Correspondence courses consist of students engaging in class material via mail or email. Students receive material and assignments through mail, and they send completed assignments back through the same method.

What Works Best For You?

Now that you have a rundown of the distance learning definition, and the different kinds that are available, you can decide whether it’s a right fit for you and your life. Many students find distance learning to be a fulfilling and practical way to receive quality education, without needing to attend a traditional university.

Whether you’re looking for a program that will allow you to work or raise a family, or whether you might have a condition that keeps you at home most of the time, distance learning can be a great way to learn valuable knowledge and tools for your future.

Related Articles

Distance Learning: What It Is and Why It’s Important

Table of Contents

With the rising trend and increasing popularity of e-learning, especially after the emergence of a global pandemic, a question that arises is how it all started out.

As research shows, distance learning began with lessons via postcards, and in the 1840s, Issac Pitman was the first to provide students with correspondence courses.

Since then, distance learning – along with new information technology, has been adopted by universities that wanted to extend their market beyond their immediate areas around their location. An example was The University of London, which wanted to offer lower higher education costs to financially disadvantaged students.

Now online courses are increasingly widespread amongst and pursued by curious people who want to broaden their knowledge or develop their workplace skills. Some people take online courses to supplement their traditional classroom courses and pursue undergraduate and graduate degrees.

In this article, we explain what distance learning is and dive into its different types. We are referring to its benefits and discussing how online schools or individual instructors can achieve a successful distance learning program.

Table of contents

What is distance learning, why is distance learning important, distance learning: trend or the future of learning, how to choose a distance learning management system.

Distance learning -also known as distance education- is the type of education that is conducted beyond the traditional classroom setting, physical space, and time and is aided by technology. As a term, it refers to the courses that can be studied without needing learners to be physically present at the school, college, or university.

Online educational tools allow students and instructors to interact synchronously or asynchronously and give endless training opportunities with distance learning courses or hybrid courses. For students who embrace distance learning, home is their most preferred location option.

Distance learning is available for all academic levels, including undergraduate, postgraduate, or master’s degree programs and doctorates. It can also be used in multiple educational modalities.

How does distance learning work?

Online courses are delivered using a Learning Management System (LMS) . With LearnWorlds, for example, online learners can attend live lessons, and complete their coursework – including exams, directly online.

Experiences in a well-trusted LMS may include study guides and self-paced assignments, video lectures, audio recordings, video conferencing, interactive learning objects, essential course materials, and live chat with fellow students or asynchronous discussions.

Finally, a learning group (discussion forums) is established in an LMS, composed of student-teacher interactions and instructional resources. The exciting thing here is that despite the popularity of Facebook, YouTube, or other social media groups, online communities of like-minded persons in LMSs are still lively and engaging. So, these are the main capabilities offered within a modern LMS.

Depending on the audience you are providing instruction to, you will need to look for the LMS features that best match your requirements.

Online learning in the corporate setting – online training – refers to educational programs tailored to particular target groups. For example, industries now invest more and more in customer or employee training. The training aims to develop specific skills so that people can perform better in a particular field, or in the case of customers, increase their knowledge regarding a product.

If you want to use an LMS for training your customers, for example, this is what you can get:

Start using a distance learning tool (LMS) today, and see the benefits it can offer to your learners:

Your professional looking Academy in a few clicks

How do distance learning programs work.

One of the major advantages of distance learning programs is that learners can connect at any time in an e-learning environment and learn at their own pace.

Most e-learning programs are asynchronous. However, they often include synchronous tools that allow learners to interact face-to-face and collaborate at scheduled times. Because most e-learning environments are generally self-paced, students must be very self-motivated to study by themselves and also manage their time effectively.

Blended learning approaches, shared social spaces in the form of blogs, and collaboratively edited documents are also used in online educational settings.

What are the advantages of distance learning?

In an online environment, information can be accessed when it is most convenient for the learner, asynchronously. This is a huge advantage for people who want to access remote learning and participate in online programs they wouldn’t be able to attend in person. Here are the key benefits of distance learning:

• High-quality and personalized course content : College faculty, scholars, and instructional designers tend to design more academically proven and high-quality curricula because of the massive enrollment of people in courses. Plus, the curriculum is tailored for highly motivated and goal-oriented learners.
• Increased accessibility and immediate feedback : The learning experience is technologically enhanced, and students with disabilities now have endless accessible learning options. Learners can attend the virtual classroom and meet with their instructors and peers in real-time. Not only that, but they have better access to feedback – both in verbal and written form.
• Reduced costs and transportation expenses : eLearning also reduces training costs by removing costly transportation expenses, physical location costs, and other amenities that traditional education offers. Distance learning is seamlessly cost- and time-efficient, allowing participants to combine full-time work, family, and studies. In this light, the online teaching approach also makes instruction more convenient.

What are the different types of distance learning?

Overall, there are 3 types of distance learning:

• Education programs that are delivered entirely online . Also known as virtual learning, through which colleges and universities allow students to receive instruction only from home. These programs will require a computer and an internet connection. The instruction is mostly based on video conferencing, among other teaching methods.
• Hybrid education programs . Learning takes place both via face-to-face interaction and online. In this type of learning, students must attend campus at certain times a month and be present in the online learning environment. Each hybrid program has a different amount of time required on campus.
• Open schedule or fixed-time online courses . This refers to online course instruction. Open schedule online courses are asynchronous, which means learners get a set of deadlines, but they have the freedom to complete the coursework at their own pace. Fixed-time online courses require learners to log in to the elearning platform and complete pre-scheduled classroom activities at designated times.

Distance learning provides educational opportunities aimed toward personal and professional development and also satisfies an individual’s thirst for knowledge. Now, individuals can pursue their dreams or enjoy learning a subject they love.

For example, finding a niche and the job of your dreams is challenging. New online educational programs are now available (some even free) to offer professional qualifications.

With distance learning, hundreds of students now earn an AA Degree or Certificate by entirely participating in online courses. Coastline college , for example, offers over 70 online degrees and certificate programs that you can take fully online.

Distance Learning vs. Online Learning

There is a fine line between distance learning and online learning that often gets blurred. Let’s get this straight.

Distance learning (also known as remote learning ) has a higher level of accountability but requires scheduled class times . It replaces face-to-face classroom learning and is often used when in-person learning is not feasible (e.g., during the COVID-19 pandemic). Distance learning strives to re-create the classroom environment , meaning that a student logs in to the virtual classroom environment at scheduled times to view lectures or participate in group learning activities.

Online learning allows you to learn from the comfort of your home , often around a schedule that fits your needs and also sometimes in combination with some in-person meetings. There are plenty of tools that can keep learners engaged, for example, interactive flipbooks offer a more immersive learning experience, with features such as clickable links, embedded quizzes, and video explainers, allowing learners to actively engage with the content they’re studying. Online learning environments are more flexible but require learners to be self-motivated.

As long as distance learning offers the same quality as traditional education does, it will continue to be embraced by learners; and studies show that it does. One more reason for students to go online is that the web has forever changed the way we interact and find information. Even after the coronavirus, online learning has proven to be as reliable as it is accessible and flexible, making it so appealing still.

According to a recent study conducted by the Sloan Consortium , the national data on college and university enrollments has been declining in the latest years. Therefore, the rate of students pursuing an online education is significant for the educational landscape.

Universities benefit from adding students worldwide. Massive open online courses (MOOCs) that are provided for financial aid are now characterized by huge enrollments in microlearning- the use of short videotaped lectures and peer assessments. Students can access the learning materials wherever and whenever they choose. By using a state-of-the-art online learning platform , instructors can make their academy the must-go learning destination.

Examples of distance learning academies

To help you get a complete – 360-degree view of distance learning, here is a list of some online schools that have been using distance learning effectively.

• The London School of Economics and Political Science (LSE)
• Broward College
• Houston Community College
• Valencia College
• Central Piedmont Community College
• St. Petersburg College
• Portland Community College
• Austin Community College District
• College of Southern Nevada
• Northern Virginia Community College
• Mercy College of Ohio
• Coastline College

Most of the above academies have used LearnWorlds to build their online presence. With the plethora of online learning platforms and learning management systems – LMSs, out there, it’s easy to create neat learning experiences today.

Choosing a remote training platform is easy as long as you know what you are looking for in terms of features and have a rough idea of what learning experience you want to provide to learners. With LearnWorlds, within just a few clicks, you’ll get your school website with powerful features:

• Student management
• Control of the learning process
• Integrated payment processing
• Multiple types of activities

But, before you start, you will need an account . LearnWorlds comes with a 30-day trial, so it is worth trying out. The platform offers more than any LMS platform can provide, allowing you to create unique interactive learning experiences.

Backed up with the ability to create flexible online courses , LearnWorlds allows you to add any learning activity you want to your training course. These activities include text, quizzes, assignments , Certificates, PDFs, live webinars , audio recordings, interactive video , HTML5, and every embeddable content.

LearnWorlds is a self-paced learning environment and can work excellently either for synchronous communication with learners or asynchronous.

An instructor can decide on the navigation settings in their courses, choosing, for example, whether they want students to take a linear path in the units or free navigation within the units.

The platform also offers a powerful gradebook . With it, you can overview your learners’ performance and check who has completed the training or not, what grades they got, and which answers they gave to each question.

Community learning is vital for every online educational program. Making students feel that they are part of a learning community where they can communicate provides more learning opportunities. That’s why in LearnWorlds, you can find a built-in social network page where you can empower your strong community and allow learners to be bonded and actively involved in the school.

With LearnWorlds, you can also change the most important settings in your school. Having your own online academy needs tools and options that can give you absolute control:

• Login settings : add more fields and control registrations
• School language settings : choose your native language
• Copyright protection settings : secure your online school
• The GDPR toolkit : make your school compliant

You can learn more about these in our step-by-step guide on how to start an online school. Finally, with LearnWorlds, you can send automated emails to your learners whenever certain events occur in your academy (for example, registration emails, enrollment emails, subscription emails, learners notifications, admin settings, and email signatures).

These and many other features make up the LearnWorlds platform. For a closer look at what the platform offers, go through our list of features .

Final Thoughts

Online education is undoubtedly here to stay with the rising advances in information technology. Online courses have proven to deliver educational information effectively and inexpensively, so it’s a cut-out solution for universities, colleges, and other training agents.

Online learning programs are becoming increasingly popular, and the trend is only growing.

Starting an online academy is not as difficult as it once was. First off, start building a website that will host your online school. LearnWorlds offers a one-stop solution for building an educational website, creating your online courses,, and delivering a great experience to your learning community.

If you are looking to launch an online education program, we are here to help you!

What’s good about distance learning?

Distance learning is packed with many benefits, including:

• Increased flexibility and work-life balance
• Increased accessibility for special needs students
• Availability of learning materials (24/7)
• Less to no commuting
• Saves valuable time and money
• Can be self-paced
• High-quality and personalized learning
• Develops technical skills with the use of technology

What is a distance learning course?

A distance learning course is a course that offers access to education online. Often this includes studying towards a degree offered by an online school, college, or university. Instead of students attending lectures physically, they get to study at home using the internet and/or other tools like an elearning platform or LMS.

What are the best distance learning online activities?

These are the best distance learning activities that can drive lots of engagement:

• Synchronous and asynchronous online discussions,
• Online self-assessments
• Blogs and wikis
• Virtual field trips or virtual labs
• Real-world case studies
• Simulations
• Problem-solving exercises
• Concept mapping
• Webinars, videos, and other interactive learning objects.

Can I create a distance learning online university?

Yes, you can build your own online school and teach any subject you want or consider yourself to be an expert in. With an online course platform like LearnWorlds, you get all the tools you need to become a successful online instructor and teach students worldwide.

How does distance learning affect students?

Distance learning helps students access high-quality content and work through their studies at their own pace, with greater flexibility, and from the comfort of their homes. To succeed, however, students must be self-motivated to study alone, complete their assignments on time, and prioritize their tasks.

Can distance learning help special needs students who can’t travel easily?

Yes, distance learning can provide additional aid to special needs students who can’t travel easily. With the help of technology and the right learning tools, they can attend online sessions from their computer without the need to travel long distances or be in the same physical location as their instructors.

Kyriaki Raouna

Kyriaki is a Content Creator for the LearnWorlds team writing about marketing and e-learning, helping course creators on their journey to create, market, and sell their online courses. Equipped with a degree in Career Guidance, she has a strong background in education management and career success. In her free time, she gets crafty and musical.

What is Distance Learning? The Complete Guide

In 2017, 6.6 million students enrolled in distance learning. Following the global pandemic in 2020, that number skyrocketed to over 400 million students. 

Online learning from a distance has become a go-to method of education, opening up countless new opportunities and access to education that many didn’t think possible prior to the forced shift brought on by the changes Covid-19 thrust onto the world. 

“Multiple studies suggest that most students are already confident that technology-enabled learning works, but this has probably been a difficult transition for some faculty members. I am hoping that universities and faculty members will embrace the challenge and adapt,” K. Holly Shiflett, director, North American partnerships, FutureLearn, said in a recent interview . 

The shift to online distance learning has been a truly transformative moment for many educators and students alike. 

Unfortunately, it can be a challenge for many educators to find the time to create effective online courses , and the continuous developments in the technology that supports it can make it difficult to keep up.

That’s why we’re here to help!

Create Engaging Course Videos with Camtasia

Whether you’re just getting started with video creation or have done it before, Camtasia’s workflow is intuitive and easy to learn.

Try Camtasia for Free

What you’ll learn in this guide

This guide will help universities and faculty members understand and embrace the challenge of distance learning. 

What is distance learning?

• What are the types of distance learning?

Who uses distance learning?

What are the advantages of distance learning, what are the disadvantages of distance learning, are distance learning degrees recognized, what is the future of distance learning.

The definition of distance learning 

“Distance learning” refers to any education provided without the teacher and students being physically present together. 

In the past, high schools and universities offered correspondence courses as a method of distance learning. Course materials were often sent to a student by mail and assignments were completed online or returned to the teacher by mail. 

More recently, distance learning programs are making use of the incredible opportunities afforded by modern technology and offer very intimate and effective learning opportunities in all manner of distance education courses. From kindergarten to elementary school to university, effective distance learning is now a viable option. 

Distance learning versus regular learning, what are the differences?

There are, of course, some significant differences between distance learning and traditional learning; the most obvious being the absence of a requirement for physical attendance in a specific location. 

When participating in distance learning, students experience significantly more freedom in their approach to learning. This can be a positive aspect in that students can choose courses based on their own schedules, the teaching style offered, and the modalities used.

Non-traditional learners can create a learning environment that works well for them rather than having to fit themselves into the traditional educational mold. 

On the flip side of that freedom however lies the requirement for students to be highly disciplined with their studies. In the case of distance learning in a university context, the implications may be less severe but for elementary distance learning and especially distance learning for kindergarten there is a need for some level of adult supervision to ensure the best chance at success.

Synchronous and Asynchronous Distance Learning – What’s the difference?

Distance learning falls into two main categories:

Synchronous learning

Asynchronous learning.

You should understand the difference between synchronous vs asynchronous . Different types of distance learning fall into one or both of these camps. 

Synchronous means “at the same time.” It refers to a method of education delivery that happens in real time. It requires live communication online. It uses technology, such as teleconferencing, to achieve this.

Synchronous learning proves less flexible than other forms of distance learning. After all, students must meet with their instructor and sometimes their classmates at pre-scheduled times.

This approach limits the student’s ability to learn at their own pace. It may frustrate some learners who crave the freedom of the asynchronous classroom.

As for asynchronous distance education? Students receive clusters of weekly deadlines. They have the freedom to work at their own speed.

Asynchronous distance learning comes with more opportunities for student interaction.

Students can access course content beyond the scheduled meeting or class time and interact through online conversations, quizzes, or video comments on their own schedule.

Both faculty and students benefit from the flexibility of asynchronous learning as it allows them to create and consume content when it’s convenient for them.

Benefits of asynchronous learning

In today’s world, both professors and students realize that external factors contribute to odd hours and disjointed schedules.

The flexibility of asynchronous learning allows them to create and consume content when it’s convenient for them and learning materials can be accessed at any time, anywhere.

• Reach and engagement

With hectic and unpredictable schedules, faculty can extend course content beyond the scheduled meeting and class time through pre-recorded videos and other content.

Faculty can leverage live recordings or create videos, and then get analytics, generate captions, have conversations, add quizzes, and integrate content right into a Learning management Software (LMS).

• Student motivation

Asynchronous teaching methods help motivate students to review content on their own time and on whatever device they prefer. 

Students can go at their own pace and when it’s convenient for them. Self-paced learning accommodates various learning needs and preferences and enhances student success.

Students can then refer back to the content to study for exams, have discussions, and consult the content beyond the confines of a live lecture.

• Complement synchronous learning experience

There’s always a need for virtual, live interaction, but asynchronous communication complements that to extend live sessions beyond a singular class.

For example, rather than simply having a Zoom meeting, professors can do a lot more with a recording. They can:

• Post the Zoom recording for rewatching
• Interact around the content
• Get analytics on participation
• Generate captions for accessibility
• Add quizzes
• Integrate with an LMS

A tool like TechSmith Camtasia will save you steps and help create better content for online courses.

What are the different types of distance learning?

Next, let’s explore various types of distance learning. These approaches to education can be synchronous or asynchronous. Some fall into both categories.

These types of distance learning include:

Video conferencing

Hybrid distance education, open schedule online courses, fixed-time online courses.

Let’s dive into what each of these types of distance learning entails. 

Video conferencing is traditionally a meeting where two or more participants use video to connect over the internet. This is a form of synchronous communication. Using tools like Zoom, Blackboard Collaborate, Adobe Connect, or other conferencing software, teachers and students interact together no matter where they are located. 

Video conferencing enhances student-instructor interactions and provides a structure for lesson planning. It remains a vital component of distance learning.

Hybrid distance education combines synchronous and asynchronous methods. Students receive deadlines to complete assignments and exams. Then, they work at their own pace.

They submit assignments through online forums. They maintain contact with their instructor. Yet, they work at their own pace. As students progress, they gain access to new modules. 

Who thrives with hybrid distance education? Students that love independence.

Under the asynchronous category, you’ll find open schedule online instruction. Such courses provide students with plenty of freedom. To complete coursework, students receive:

• Online textbook(s)
• Bulletin boards
• Email 

Students are given a set of deadlines. Then, the instructor lets them schedule at their own pace. Students who value learning independently excel with this format. It requires significant self-discipline and motivation, though.

Students who lack the right skill set may find this approach daunting. They may feel overwhelmed by the presentation of the material. They may lack the motivation to work through the course in an effective way. 

What’s the most common format for distance learning? Fixed-time online courses. 

How do they work? Students log in to the learning site at designated times. They must complete pre-scheduled classroom activities at a specific pace.

These activities often include chats and discussion forums. Fixed-time online courses encourage student interaction. But there’s little room for self-pacing.

Distance learning programs are used by a wide variety of people for many reasons. The two groups making up the largest segment of the distance learning population are students (from elementary school all the way through to university) and professionals. 

Distance learning courses are offered for students of all ages. High school students can take additional courses to speed up graduation, students in University or College can attend a school anywhere in the world without the relocation expense, and working professionals can enhance their skills by accessing distance learning programs.

Distance learning can make education more accessible for learners in rural areas or those who experience challenges with traditional education.

Distance learning activities have been put to good use in the training and onboarding for many companies. For those that are spread out globally or who have remote workers, distance learning creates an opportunity for consistency in employee experience across the board. Full training regimens can be provided using distance learning programs.

Of course traditional, face-to-face learning is wonderful in many ways, but there are also a lot of advantages to distance learning. 

Flexibility

One of the strongest advantages of distance education has to be the flexibility offered. 

Students can choose from structured distance learning opportunities with live teaching and real-time access to the teacher, or unstructured distance learning courses that can easily be adapted around a busy schedule. 

Distance learning programs can be completed from anywhere in the world. There are formats to fit many different learning styles. Most students will find an option for distance education that matches their needs. 

Easy Access

Distance education has created a new opportunity for students who may have had difficulty accessing education in its traditional format. Whether this was due to remote location, or disability, distance learning removes the barriers associated with attending in-person classes. 

Ease of access to global learning opportunities has opened up as well, with distance learning in universities and colleges making international study an option for many more students. 

It’s also created the opportunity for lifelong learners from all over the world to access courses and curriculum presented by teachers they would not have had access to otherwise. 

Money and time savings

Distance learning has rendered education much less financially draining and much more time-effective. 

Accessing distance education programs for University and College cuts tuition by 50% as compared to a traditional on-campus experience. 

Because so many of the costs related to infrastructure and logistics are removed with distance education, the costs to access it are significantly lower than comparable traditional education models.

Additionally, there are savings related to time spent as well – of course travel time is relevant for students, but on the teachers’ side, the ability to record and repurpose lessons results in an impressive amount of time saved as well.

Adaptability and freedom

Unlike the traditional education model, distance learning is easily adapted to the lifestyle and learning needs of the student. 

Lesson schedules, teaching methods and the learning environment are all adaptable to each individual student through distance learning courses in a way that is impossible within in-person education. 

Students from kindergarten to elementary school to university can use the time, space, and pace-related freedoms of distance learning to find levels of success they may not in a traditional structure.

Earning while learning

Working professionals of all ages can use distance learning to earn a higher degree of education, or an entirely new skillset while maintaining their day-to-day working lives. 

Many Distance learning universities and colleges offer asynchronous programs, allowing degrees and certifications to be earned outside of an individual’s working time. 

Subject Matter experts from across all topics offer self-paced distance learning courses that can enhance an individual’s knowledge and credentials without interrupting their ability to earn an income.

The benefits of distance learning are clear, but there are some warnings when it comes to this learning approach, too. Let’s consider some of the disadvantages. 

Lack of Social Interaction

The amount of social interaction offered in distance learning activities is far less than in the traditional education model. 

Without the requirement to attend a brick-and-mortar location, students miss out on the ability to work directly with peers. This characteristic of distance learning can affect children most notably, particularly for children performing distance learning activities in elementary school when peer interaction is such a cornerstone. 

One way that this potential disadvantage can be limited is in the use of video as a communication tool, particularly regarding the provision of feedback. 

Receiving feedback (think a graded test, a written comment, a marked up essay, etc) as part of distance learning often happens without the familiar social cues that help one understand the context and can feel somewhat abrasive. It can leave a student’s mind swimming in questions and uncertainty – particularly when the feedback is not entirely positive.  

However, when video is used to provide the feedback, these social cues are present. Being able to hear the teacher’s tone and see their facial expressions can make a world of difference and can reintegrate some of the social interaction that may be lacking in a distance learning experience. 

High Chances of Distraction

Distraction can be a challenge for students engaged in distance learning programs. This can show up in many ways.

For one, students face a higher risk of online distraction. Without face-to-face meetings, students can lose track of deadlines and motivation.

Students who work well on their own may easily surmount these obstacles. Students who have trouble prioritizing may stumble. So will those who lack organizational and scheduling skills. 

Self-motivation and focus are essential skills for success in distance learning activities.

Complicated Technology

Overdependence on technology is a challenge with distance learning. 

Students must have reliable access to tools like a computer, webcam, and a stable internet connection. 

Any malfunction of hardware or software on either the student’s or teacher’s end can result in learning coming to a complete standstill. 

To be successful, students or their caregivers must have a moderate level of comfort with technology. This is a requirement for modern distance learning at any level. 

Questionable Credibility of Online Degrees

Many employers will not accept a degree or certification from a distance learning program. This is a result of a lingering stigma around distance learning.

Not all teachers are skilled or comfortable with teaching in an online environment. This contributes to inconsistency with course materials and areas of focus. 

A perceived lack of proper assessment is another factor contributing to this challenge with the credibility of credentials gained through distance learning programs.

Hidden student costs

While the reduced overhead costs to institutions often result in a lower cost of learning for students engaged in distance learning, there are some hidden costs associated with this type of learning. 

These expenses include:

• Gaining access to a reliable computer
• Having an internet connection
• Buying a web camera (in some instances)
• Computer maintenance
• Utilities (e.g., electricity for internet services)

Not all students have access to these resources. Distance learning can put them at a distinct disadvantage.

How to Shift to Online Teaching: The Ultimate Guide

In this FREE guide, we cover how to humanize course content, leverage asynchronous learning, and transform your online teaching.

Download now!

In recent years, distance learning has gained in popularity. In the fall of 2017, 3.1 million higher education students enrolled in online programs. And there is a rising proportion of students studying fully online who are enrolled within 50 miles of their home. 

Distance learning has gained widespread credibility and acceptance, too. Top online universities are comparable to their on-campus counterparts. 

Nonetheless, students should still keep an eye out for scammers. Before closing a distance learning program, they should explore the institution’s accreditation. 

What does accreditation look like? While it may vary, accreditation occurs at three levels:

• Programmatic accreditation
• Regional accreditation
• National accreditation

Programmatic accreditation attests to the validity of certain programs of study. 

Regional accreditation means regional agencies have endorsed specific fields of study.

National accreditation means the program meets federal accreditation requirements. 

Does distance learning have value?

Absolutely. While distance learning was once considered an inferior substitute for traditional education, it is now considered by many to be outperforming traditional classroom instruction. 

This is largely because of video and technology. Video helps make distance learning feel personable and helps keep students engaged .

Not only are students more successful, but they prefer remote learning.

77% of academic leaders rate online education as equal or superior. And 69% of chief academic officers agree. 

Distance learning is a vital part of long-term education strategies. 

What makes a good distance learning program?

Not all distance learning courses are created equal. There are some key factors to consider when searching for high-quality distance learning courses – Let’s look at what those are. 

Proper Pacing

The best distance learning courses are those that are well-paced. 

Students should not find themselves bored or overwhelmed by the lessons, projects, or course material. 

The distance learning activities should include large projects for which students have ample time to prepare, with smaller meaningful assignments peppered amongst them to retain engagement and interest. 

Multimedia Integration

Excellence in distance learning courses can be tied to the effective use of multimedia learning aids. 

Integrating podcasts, videos, and interactive activities can be very engaging for students and serve multiple learning styles. 

When purposefully used, distance learning activities that incorporate multimedia tools can assist with the retention of course material. In fact, research shows that two-thirds (67%) of people understand information better when communicated visually.

Quality Content

Low-quality content, like endless textbook reading assignments, monotonous lectures, and stock assessments lacking creativity, results in low retention of distance learning course material. 

Distance learning activities that center on high-quality content, like engaging videos, passionate lectures, and interactive websites help students to maintain interest, retain information, and find more success. 

Self-Directed Learning

Distance learning courses which offer students the ability to make their own discoveries, complete projects in the way that works best for them, and focus on the areas of study that they find most interesting are the courses that lead to the highest success.

Community Connection

Great distance learning programs recognize that students are often negatively affected by the lack of social interaction within this style of education and intentionally incorporate the community into their curriculum.

Effective courses include group projects where students have to work together, and opportunities for classmates to connect using digital tools. 

Multiple Learning Modalities

The best distance learning opportunities integrate a range of learning modalities to allow students to learn in the way that works best for them. Some students retain and engage with information best visually, while others need to hear information spoken out loud.

Modalities included in the best distance learning courses will include visual, auditory, and kinesthetic, among others.

Intuitive Navigation

For students to see success, distance learning programs should be intuitive to navigate. Ideally, this will be vetted by a third party. 

Students should be presented with well-laid-out course content that lets them easily see what to do and when. Access to required resources and information should never be a challenge within a well-formatted distance learning course. 

Reliable Technology

The most effective distance learning programs use technology that is as universally applicable as possible. Students should not have to download and learn new or unreliable apps, plugins, or extensions to access or engage with course material. 

Flashy additions of technology, while potentially interesting, can negatively impact the student experience in distance learning courses. 

Room for Additional Exploration

Striking a balance between providing students with opportunities to dive into additional resources and overwhelming them with too many exploratory opportunities is an important balance for distance learning programs to strike. 

Creating a clear distinction between required course material and optional enhancement activities is crucial in providing a compelling distance learning experience.

Recommended reading : 7 things to build online courses .

While distance learning has been in use for centuries, the broad availability of internet services combined with the Covid-19 pandemic has resulted in an intense increase in its adoption since 2020. 

It has been clearly illustrated that physical presence in a classroom is no longer the only option for effective learning. 

Distance learning for students from elementary school to universities and colleges has come a long way, with no signs of its growth slowing down. 

With the rise of remote and hybrid work, workplaces have continued to adopt distance learning practices as well. 

Learning Management Systems and easily incorporated tools for creating educational content like Snagit and Camtasia have made it easier than ever to provide quality education in the workplace, regardless of distance. The great news is that each of these tools offers a free trial so you can start creating amazing learning resources right now!

While distance learning is unlikely to fully replace in-person instruction it’s certainly an effective tool that will continue to be developed and integrated into an increasing number of scenarios.

Ryan Knott is a Marketing Content Strategist at TechSmith, where he creates content about easy, effective, and efficient video creation, editing, and tips and tricks, as well as audio editing for creators of all kinds. He/him.

• How to Create a Video Storyboard
• How to Make a YouTube Video (Intermediate Guide)
• How to Make a YouTube Channel: Step-by-Step Guide

Subscribe to TechSmith’s Newsletter

Join over 200,000 people who get actionable tips and expert advice every month in the TechSmith Newsletter.

• e-Learning Glossary

What is Distance Education?

Discover the fundamentals of distance education including how it works, its benefits, and how to choose the right program for you. Learn tips for success in online courses and find out if distance education is the right fit for you.

Distance education, also known as online or remote learning, allows students to study and earn a degree or certification from a distance. However, this type of education has become increasingly popular in recent years as technology has made it easier to access and complete coursework from anywhere in the world.

How does distance education work?

Distance education typically involves students completing coursework online through a learning management system (LMS) or a virtual classroom . Students can also access course materials, assignments, and assessments at their own pace and schedule.

Moreover, communication with instructors and other students is often facilitated through discussion forums, email, and other online tools. Some distance education programs also offer live, interactive sessions that allow students to participate in real-time discussions and activities.

Distance education offers several advantages over traditional classroom-based education, including:

• Flexibility: It allows students to complete coursework on their own schedule, which is ideal for those with busy lifestyles or those who need to balance work and study.
• Accessibility: Distance education programs are also available to anyone with an internet connection, making it easier for people in remote or underserved areas to access quality education.
• Cost savings: They are often more affordable than traditional programs, as they don't require students to pay for room and board, transportation, and other related expenses.
• Improved educational outcomes: Research has shown that students who participate in online learning perform just as well as those who participate in traditional classroom-based learning.

How do I know if distance education is right for me?

Distance education may be a good option for you if you:

• I need to balance work and study
• Prefer to work at your own pace
• Have a busy lifestyle
• Live in a remote or underserved area
• Prefer to learn independently

How to choose the right distance learning program for you ?

When choosing a distance education program, there are several factors to consider, including:

• Accreditation: Make sure that a reputable organization accredits the program.
• Course content: Ensure that the program offers the course content and resources you need to achieve your goals.
• Faculty: Make sure the program has experienced and qualified instructors to support you throughout your studies.
• Technical support: Ensure that the program provides reliable technical support to help you with any technology-related issues that may arise.

Tips for succeeding in your online course

To ensure success in your distance education program, consider the following tips:

• Set goals and create a schedule: Establishing clear goals and a study schedule can help you stay motivated and on track.
• Participate in discussions: Engage with your instructors and other students through online discussions and forums.
• Get organized: Keep track of your assignments, deadlines, and due dates using calendars and task lists.
• Stay connected: Regularly communicate with your instructors and classmates to stay connected and stay on top of your coursework.

Distance education provides a flexible and accessible option for students pursuing their education goals. Students can achieve their educational goals while balancing work and other responsibilities with the right program and support.

Whether you're just starting your educational journey or looking to complete a degree or certification, it can be a valuable and effective option.

Learn more: What is an Online Learning Community?

Create engaging and interactive courses at scale. Start today.

Related learning terms

Formative evaluation is to assess the progress of a project/program during development to increase its efficiency, effectiveness, and success.

Just-in-time training is a process which helps us immensely. Just-in-time training has helped me to do my job better.

Predictive Analytics is a method of analyzing current and historical data to make predictions about the future.

Explore the concept of Goal Orientation and learn how it can boost productivity and success rates in professional and personal arenas.

We empower online academies to launch engaging learning experiences and improve learning outcomes through our social learning platform.

• White Label LMS
• LMS for Nonprofits
• Integrations
• Comparisons
• eLearning Glossary
• Request a demo
• Cohort-based learning
• Collaborative learning
• Corporate training
• Capability academy
• Group coaching
• Course creators platform
• Community-based learning
• Free course pricing calculator
• Free course revenues calculator
• Become a partner
• Privacy Policy
• Product Updates
• System Status

• Cambridge Dictionary +Plus

Meaning of distance learning in English

Your browser doesn't support HTML5 audio

• The university hopes to offer more evening programs and distance learning to attract more students .
• Future training programs will feature a balance of traditional face to face classroom and distance learning .
• The cost is £33,000 for the full-time MBA (one year ); or £16,795 by distance learning (over three years ).
• For people who can't make it to the center , distance learning courses are available .
• abstinence education
• abstinence programme
• abstinence-only
• academically
• adaptive learning
• homeschooler
• homeschooling
• intersession
• scholarship
• special educational needs
• virtual learning environment
• vocationally

distance learning | Business English

• distance-learning

Examples of distance learning

Translations of distance learning.

Get a quick, free translation!

Word of the Day

customer support

help and advice that a company makes available to customers when they have bought something

Varied and diverse (Talking about differences, Part 1)

What Is Distance Learning? And Why Is It So Important?

Distance learning – any form of remote education where the student is not physically present for the lesson – is booming thanks to the power of the Internet. With a variety of course types to choose from, there is a rise in flexible and affordable education options. In fact, there are a number of advantages of learning remotely over even traditional teaching models.

So keep reading to learn more about distance learning. Or go straight to our recommended distance learning tool: myViewBoard Classroom.

As the Internet blurs the line between near and far, distance learning is set to disrupt the current paradigm of education. With everything from AI-driven teaching algorithms to simple message boards, there are more options than ever to learn whatever you need to know.

While skilled educators will continue to be an integral part of every student’s life, technology will bridge the physical spaces between teachers and learners. Distance learning is already part of many institutions’ programs, and it is set to become an even larger part of the education sector . But first…

What Is Distance Learning?

Distance learning describes any learning that happens without the students being physically present in the lesson. (However, this could also apply to the teacher in certain situations.)

Historically, this described correspondence courses in which students would communicate with their schools or teachers by mail. More recently, distance education has moved online to include a huge range of systems and methods on practically any connected device.

Want to learn more about distance learning?  Defining Distance Learning

Common Types of Distance Learning

Though there are lots of learning (and teaching) options online, there are a few types that are well supported by existing systems and established pedagogies.

• Video conferencing  is a common way for teachers to interact directly with students in live lessons. This could be a one-on-one session or a class-like scenario in which multiple students connect to the teacher live.
• Synchronous learning is when all the students learn together at the same time (and often even place) but the instructor is at another location. It often features video or teleconferencing that connects teachers and learners digitally.
• Asynchronous learning is a less connected but also less constrained format. Instead of live online lessons, students are given learning tasks with deadlines. They then self-study to complete the assignments.
• Open-schedule online courses add yet another layer of flexibility. It is a type of asynchronous course setup, except there aren’t any deadlines either. This is ideal for learners with other demands on their time, such as professionals or stay-at-home parents.
• Fixed-time online courses are a type of synchronous course that requires online users to all visit a specific virtual location at a set time and place (e.g. a webinar). Unlike more rigid synchronous lessons, this does allow students from anywhere in the world to connect and interact online.
• Computer-based distance education is a fixed-time, synchronous lesson on computers, usually a computer lab. This is most common in existing institutions that already have access to the necessary devices.
• Hybrid learning is a specific type of blended learning where students are learning the same lesson in real-time (i.e. synchronous distance learning) but some of the students are physically present while others are learning remotely.

Are you more interested in live education?  Technology in the Classroom: The Complete Guide

How Is Distance Learning Different from Regular Learning?

Distance education is clearly different from regular education in terms of a student or teacher’s physical presence. But what does that mean, exactly?

For the most part, it translates into increased freedom for both learners and educators, but it also requires higher degrees of discipline and planning to successfully complete the course of study.

The enhanced freedom of remote learning is most clearly seen in the fact that students can choose courses that fit their schedules and resources. (Teachers can do the same.) And in the case of digital learning, students can also choose the location and teaching styles that best suit their needs.

The flip side of freedom, however, is the discipline required to make the most of the lessons. Students need to self-motivate in order to actually get the work done, especially in systems that don’t require them to be present in some specific time or place. Teachers also need to be better organized with contingencies should their students need additional explanation, again especially if they are not teaching live and able to “read the room.”

In certain cases, however, distance learning is not just required but the best possible option. There are times when the advantages of remote education really have a chance to shine.

Advantages of Distance Learning

Certainly, live instruction is great. The face-to-face contact lets teachers and students connect in a very authentic way, which often results in strong rapport and understanding. While not impossible, this kind of connection still seems much easier in person . So why is distance learning even a thing?

As it turns out, there are a number of advantages when learning remotely. Here are just a few.

Flexibility

The top benefit of distance education is its flexibility. Students can choose when, where, and how they learn by selecting the time, place, and medium for their education. For those who want direct, live access to teachers there are video conferencing options. But for students who may be doing their training around a job or other responsibilities, a more relaxed schedule may work better. There are options to match virtually anyone’s needs.

And thanks to the proliferation of online learning options, there is a course structure on practically any subject that a person would want to study.

Easy Access

Whether due to remote location or being differently-abled, some students lack basic access to educational facilities. Remote learning programs offer every student the opportunity to learn and improve themself in the environment they find the most effective.

Want to make a live classroom more accessible? 6 Cases of Interactive Digital Whiteboards as an Assistive Technology in Special Education

Remote learning also opens up new horizons of education in terms of international institutions. Major universities and trade schools the world over now offer recognized degrees, certificates, and professional qualifications online to learners of all ages. Or motivated people can get more basic certificates of completion everywhere from Udemy to Google Skillshop .

Thanks to the scalable nature of digital learning especially, distance learning is driving down the cost of education . Online degrees are becoming almost commonplace, and there are even accredited online-only universities that can eliminate expensive infrastructure overhead and get straight to the teaching.

What to Look for in a Distance Learning System

Regardless of whether you are an educator or a student, there are certain features that you should look for in a distance learning system to get the most out of it.

Ease of Use

Simplicity is the key. Any system you adopt to either teach or learn should be user-friendly for everyone involved. This means a clear interface and a set of certain essential features that include:

• Digital whiteboarding and annotation
• Media creation and sharing
• Screen recording with audio
• Direct student-to-teacher communication
• Multi-device compatibility

ViewSonic Education

Learning Solutions For the Future

Accreditation

The credibility of a remote learning platform is really a combination of the instructor and the platform itself. For learners, it’s important to note how well recognized that platform’s credentials are. Does it provide a recognized degree? A professional certificate? A certificate of completion? These are all things to keep in mind before enrolling.

And educators looking to adopt a remote learning system, it’s important to know what kind of accreditation that system can bestow on your behalf or on the behalf of your institution. For academic degrees or professional qualifications, recognition by outside regulatory bodies will likely be necessary.

As most distance learning systems are made to be fairly flexible in this regard, the course schedule has a lot to do with its content and not the system. Still, it’s an important factor to consider when choosing a course.

Is it a synchronous or asynchronous course? Are there deadlines or not? How long do you have to complete the entire course? And does the course’s schedule match yours?

Want to see an example of an online tool that checks all the distance learning boxes? Check out myViewBoard .

Wrapping Up

Remote education is certainly not a magic bullet and there will always be a place for in-class learning. At the same time, distance learning still has a lot of untapped potential to reach students where they are and connect educators and learners in new ways. From increased flexibility to new learning styles, it seems that the future of learning will be as diverse in time and place as it will be in thought.

RELATED ARTICLES

Technology in the Classroom  |  Dec 08 2023

Online Whiteboarding vs. Whiteboarding Apps: Which Is Better for the Classroom?

Explore digital whiteboarding in education, weighing the pros and cons of online vs. native apps to find the class setup they are best for.

Technology in the Classroom  |  Sep 06 2022

Best Monitors for Teachers: How to Use Pen Devices to Deliver Lessons 

Best monitors for teachers not only make sharing knowledge more easily but also help deliver more engaging lessons.

Education  |  Mar 03 2022

What Is a Digital Whiteboard?

Wondering what a digital whiteboard is? Let's dive in together and discover all the great possibilities it offers.

Technology in the Classroom  |  Nov 23 2021

Video Conferencing Monitors – 4 Useful Benefits for Education

Video conferencing monitors can benefit teachers and students in and outside of the classroom. Read more about the 4 ways they can help.

SELECT YOUR REGION

Asia pacific & africa.

Selecting subsets of source data for transfer learning with applications in metal additive manufacturing

• Published: 12 May 2024

Cite this article

• Yifan Tang   ORCID: orcid.org/0000-0003-3789-1639 1 ,
• Mostafa Rahmani Dehaghani 1 ,
• Pouyan Sajadi 1 &
• G. Gary Wang 1  

12 Accesses

Explore all metrics

Considering data insufficiency in metal additive manufacturing (AM), transfer learning (TL) has been adopted to extract knowledge from source domains (e.g., completed printings) to improve the modeling performance in target domains (e.g., new printings). Current applications use all accessible source data directly in TL with no regard to the similarity between source and target data. This paper proposes a systematic method to find appropriate subsets of source data based on similarities between the source and limited target datasets. Such similarity is characterized by the spatial and model distance metrics. A Pareto frontier-based source data selection method is developed, where the source data located on the Pareto frontier defined by two similarity distance metrics are selected iteratively. This method is integrated into an instance-based TL method (decision tree regression model) and a model-based TL method (fine-tuned artificial neural network). Both models are then tested on several regression tasks in metal AM. Comparison results demonstrate that (1) the source data selection method is general and supports integration with various TL methods and distance metrics, (2) compared with using all source data, the proposed method can find a subset of source data from the same domain with better TL performance in metal AM regression tasks involving different processes and machines, and (3) when multiple source domains exist, the source data selection method could find the subset from one source domain to obtain comparable or better TL performance than the model constructed using data from all source domains.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

(Adapted from Ding et al., 2022 )

Data availability

The datasets about the melt pool width in DED-LB/p and the relative densities of parts fabricated by different machines are open-accessed. The dataset about the melt pool width in DED-LB/w is available upon reasonable request from the authors.

Aboutaleb, A. M., Bian, L., Elwany, A., Shamsaei, N., Thompson, S. M., & Tapia, G. (2017). Accelerated process optimization for laser-based additive manufacturing by leveraging similar prior studies. IISE Transactions, 49 (1), 31–44. https://doi.org/10.1080/0740817X.2016.1189629

Article   Google Scholar  

Aharoni, R., & Goldberg, Y. (2020). Unsupervised domain clusters in pretrained language models. arXiv preprint: arXiv:2004.02105

Akhavan, J., Lyu, J., Mahmoud, Y., Xu, K., Vallabh, C. K. P., & Manoochehri, S. (2023). Dataset of in-situ coaxial monitoring and print’s cross-section images by direct energy deposition fabrication. Scientific Data, 10 (1), 776. https://doi.org/10.1038/s41597-023-02672-4

Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., et al. (2023). Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13 (2), 1–43. https://doi.org/10.1002/widm.1484

Bisheh, M. N., Wang, X., Chang, S. I., Lei, S., & Ma, J. (2023). Image-based characterization of laser scribing quality using transfer learning. Journal of Intelligent Manufacturing, 34 , 2307–2319. https://doi.org/10.1007/s10845-022-01926-z

Brion, D. A. J., Shen, M., & Pattinson, S. W. (2022). Automated recognition and correction of warp deformation in extrusion additive manufacturing. Additive Manufacturing, 56 , 102838. https://doi.org/10.1016/j.addma.2022.102838

Cheng, G. H., Wang, G. G., & Hwang, Y. M. (2021). Multi-objective optimization for high-dimensional expensively constrained black-box problems. Journal of Mechanical Design . https://doi.org/10.1115/1.4050749

Dai, X., Karimi, S., Hachey, B., & Paris, C. (2019). Using similarity measures to select pretraining data for NER. arXiv preprint: arXiv:1904.00585

Dai, X., Karimi, S., Hachey, B., & Paris, C. (2020). Cost-effective selection of pretraining data: a case study of pretraining BERT on social media. arXiv preprint: arXiv:2010.01150

Ding, D., He, F., Yuan, L., Pan, Z., Wang, L., & Ros, M. (2021). The first step towards intelligent wire arc additive manufacturing: An automatic bead modelling system using machine learning through industrial information integration. Journal of Industrial Information Integration, 23 , 100218. https://doi.org/10.1016/j.jii.2021.100218

Ding, Y., Ding, P., Zhao, X., Cao, Y., & Jia, M. (2022). Transfer learning for remaining useful life prediction across operating conditions based on multisource domain adaptation. IEEE/ASME Transactions on Mechatronics, 27 (5), 4143–4152. https://doi.org/10.1109/TMECH.2022.3147534

Dongshang. (2023). Stainless steel density. http://www.dsstainlesssteel.com/stainless-steel-density/ . Accessed 13 Oct 2023.

Ferreira, R. D. S. B., Sabbaghi, A., & Huang, Q. (2020). Automated geometric shape deviation modeling for additive manufacturing systems via Bayesian neural networks. IEEE Transactions on Automation Science and Engineering, 17 (2), 584–598. https://doi.org/10.1109/TASE.2019.2936821

Gao, J., Zhao, X., Chen, B., Yan, F., Guo, H., & Tang, R. (2023). AutoTransfer: instance transfer for cross-domain recommendations. In SIGIR 2023—Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval , Taipei, Taiwan, China (pp. 1478–1487). Association for Computing Machinery. https://doi.org/10.1145/3539618.3591701

Guo, H., Pasunuru, R., & Bansal, M. (2019). AutoSEM: automatic task selection and mixing in multi-task learning. arXiv preprint: arXiv:1904.04153

Jamnikar, N., Liu, S., Brice, C., & Zhang, X. (2021). Machine learning based in situ quality estimation by molten pool condition-quality relations modeling using experimental data. arXiv preprint: arXiv:2103.12066

Kang, Z., Yang, B., Yang, S., Fang, X., & Zhao, C. (2020). Online transfer learning with multiple source domains for multi-class classification. Knowledge-Based Systems, 190 , 105149. https://doi.org/10.1016/j.knosys.2019.105149

Karimi, S., Dai, X., Hassanzadeh, H., & Nguyen, A. (2017). Automatic diagnosis coding of radiology reports: a comparison of deep learning and conventional classification methods. In BioNLP 2017—SIGBioMed workshop on biomedical natural language processing, proceedings of the 16th BioNLP Workshop, Vancouver, Canada (pp. 328–332). Association for Computational Linguistics. https://doi.org/10.18653/v1/w17-2342

Kim, H., Lee, H., & Ahn, S. H. (2022). Systematic deep transfer learning method based on a small image dataset for spaghetti-shape defect monitoring of fused deposition modeling. Journal of Manufacturing Systems, 65 , 439–451. https://doi.org/10.1016/j.jmsy.2022.10.009

Kitahara, A. R., & Holm, E. A. (2018). Microstructure cluster analysis with transfer learning and unsupervised learning. Integrating Materials and Manufacturing Innovation, 7 (3), 148–156. https://doi.org/10.1007/s40192-018-0116-9

Knüttel, D., Baraldo, S., Valente, A., Wegener, K., & Carpanzano, E. (2022). Transfer learning of neural network based process models in direct metal deposition. Procedia CIRP, 107 , 863–868. https://doi.org/10.1016/j.procir.2022.05.076

Lange, L., Strötgen, J., Adel, H., & Klakow, D. (2021). To share or not to share: predicting sets of sources for model transfer learning. arXiv preprint: arXiv:2104.08078

Leung, H. C., Leung, C. S., & Wong, E. W. M. (2019). Fault and noise tolerance in the incremental extreme learning machine. IEEE Access, 7 , 155171–155183. https://doi.org/10.1109/ACCESS.2019.2948059

Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 33 (12), 6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827

Lin, Y. H., Chen, C. Y., Lee, J., Li, Z., Zhang, Y., Xia, M., et al. (2019). Choosing transfer languages for cross-lingual learning. arXiv preprint: arXiv:1905.12688

Liu, M., Song, Y., Zou, H., & Zhang, T. (2020). Reinforced training data selection for domain adaptation. In ACL 2019—57th annual meeting of the association for computational linguistics, Florence, Italy (pp. 1957–1968). Association for Computational Linguistics. https://doi.org/10.18653/v1/p19-1189

Liu, R., Liu, S., & Zhang, X. (2021a). A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing. International Journal of Advanced Manufacturing Technology, 113 (7–8), 1943–1958. https://doi.org/10.1007/s00170-021-06640-3

Liu, S., Stebner, A. P., Kappes, B. B., & Zhang, X. (2021b). Machine learning for knowledge transfer across multiple metals additive manufacturing printers. Additive Manufacturing, 39 , 101877. https://doi.org/10.1016/j.addma.2021.101877

Lu, H., Wu, J., Ruan, Y., Qian, F., Meng, H., Gao, Y., & Xu, T. (2023). A multi-source transfer learning model based on LSTM and domain adaptation for building energy prediction. International Journal of Electrical Power and Energy Systems, 149 , 109024. https://doi.org/10.1016/j.ijepes.2023.109024

MatWeb. (2023). Duplex stainless steel 2209. https://www.matweb.com/search/datasheet.aspx?matguid=e4df7ef1593f4f518bd3b26667a0aa56 . Accessed 13 Oct 2023.

Mehta, M., & Shao, C. (2022). Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing. Journal of Manufacturing Systems, 64 , 197–210. https://doi.org/10.1016/j.jmsy.2022.06.010

Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., & Joulin, A. (2017). Advances in pre-training distributed word representations. arXiv preprint: arXiv:1712.09405

Milner, B. B., Gradl, P., Snedden, G., Brooks, M., Pitot, J., Lopez, E., et al. (2021). Metal additive manufacturing in aerospace: A review. Materials and Design, 209 , 110008. https://doi.org/10.1016/j.matdes.2021.110008

Ngatchou, P., Zarei, A., & El-Sharkawi, M. A. (2005). Pareto multi objective optimization. In Proceedings of the 13th international conference on intelligent systems application to power systems, Arlington, VA, USA (pp. 84–91). IEEE. https://doi.org/10.1109/ISAP.2005.1599245

Olleak, A., & Xi, Z. (2020). Calibration and validation framework for selective laser melting process based on multi-fidelity models and limited experiment data. Journal of Mechanical Design, Transactions of the ASME, 142 (8), 1–13. https://doi.org/10.1115/1.4045744

Ontañón, S. (2020). An overview of distance and similarity functions for structured data. Artificial Intelligence Review, 53 (7), 5309–5351. https://doi.org/10.1007/s10462-020-09821-w

Pandita, P., Ghosh, S., Gupta, V. K., Meshkov, A., & Wang, L. (2022). Application of deep transfer learning and uncertainty quantification for process identification in powder bed fusion. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 8 (1), 1–12. https://doi.org/10.1115/1.4051748

Pandiyan, V., Drissi-Daoudi, R., Shevchik, S., Masinelli, G., Le-Quang, T., Logé, R., & Wasmer, K. (2022). Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process. Journal of Materials Processing Technology . https://doi.org/10.1016/j.jmatprotec.2022.117531

Pang, Y., Cao, Y., Chu, Y., Liu, M., Snyder, K., MacKenzie, D., & Cao, C. (2020). Additive manufacturing of batteries. Advanced Functional Materials, 30 (1), 1–22. https://doi.org/10.1002/adfm.201906244

Pardoe, D., & Stone, P. (2010). Boosting for regression transfer. In Proceedings of the 27th international conference on machine learning, Haifa, Israel (pp. 863–870). Association for Computing Machinery. https://doi.org/10.5555/3104322.3104432

Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C. L., Rosen, D. W., et al. (2022). Research and application of machine learning for additive manufacturing. Additive Manufacturing . https://doi.org/10.1016/j.addma.2022.102691

Rahmani Dehaghani, M., Sahraeidolatkhaneh, A., Nilsen, M., Sikström, F., Sajadi, P., Tang, Y., & Wang, G. G. (2024). System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-property modeling scheme. Journal of Manufacturing Processes, 112 , 1–13. https://doi.org/10.1016/j.jmapro.2024.01.029

Ren, J. (2018). Two-stage-TrAdaboost.R2. https://github.com/jay15summer/Two-stage-TrAdaboost.R2 . Accessed 20 Jan 2020.

Ren, J., & Wang, H. (2019). Surface variation modeling by fusing multiresolution spatially nonstationary data under a transfer learning framework. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 141 (1), 011002. https://doi.org/10.1115/1.4041425

Ren, J., Wei, A. T., Jiang, Z., Wang, H., & Wang, X. (2021). Improved modeling of kinematics-induced geometric variations in extrusion-based additive manufacturing through between-printer transfer learning. IEEE Transactions on Automation Science and Engineering, 19 (3), 2310–2321. https://doi.org/10.1109/TASE.2021.3063389

Ren, K., Chew, Y., Zhang, Y. F., Fuh, J. Y. H., & Bi, G. J. (2020). Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning. Computer Methods in Applied Mechanics and Engineering, 362 , 112734. https://doi.org/10.1016/j.cma.2019.112734

Ruder, S., & Plank, B. (2017). Learning to select data for transfer learning with Bayesian optimization. arXiv preprint: arXiv:1707.05246

Sabbaghi, A., & Huang, Q. (2018). Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables. The Annals of Applied Statistics, 12 (4), 2409–2429. https://doi.org/10.1214/18-AOAS1158

Sabbaghi, A., Huang, Q., & Dasgupta, T. (2018). Bayesian model building from small samples of disparate data for capturing in-plane deviation in additive manufacturing. Technometrics, 60 (4), 532–544. https://doi.org/10.1080/00401706.2017.1391715

Schönemann, P. H. (1966). A generalized solution of the orthogonal procrustes problem. Psychometrika, 31 (1), 1–10. https://doi.org/10.1007/BF02289451

Senanayaka, A., Tian, W., Falls, T. C., & Bian, L. (2023). Understanding the effects of process conditions on thermal–defect relationship: a transfer machine learning approach. Journal of Manufacturing Science and Engineering . https://doi.org/10.1115/1.4057052

Shin, S. J., Lee, J. H., Sainand, J., & Kim, D. B. (2024). Material-adaptive anomaly detection using property-concatenated transfer learning in wire arc additive manufacturing. International Journal of Precision Engineering and Manufacturing, 25 , 383–408. https://doi.org/10.1007/s12541-023-00924-2

Tang, Y., Rahmani Dehaghani, M., & Wang, G. G. (2023a). Comparison of transfer learning based additive manufacturing models via a case study. arXiv preprint: arXiv:2305.11181

Tang, Y., Rahmani Dehaghani, M., & Wang, G. G. (2023b). Review of transfer learning in modeling additive manufacturing processes. Additive Manufacturing, 61 , 103357. https://doi.org/10.1016/j.addma.2022.103357

Tian, J., Han, D., Li, M., & Shi, P. (2022). A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis. Knowledge-Based Systems, 243 , 108466. https://doi.org/10.1016/j.knosys.2022.108466

Tian, Y., Sehovac, L., & Grolinger, K. (2019). Similarity-based chained transfer learning for energy forecasting with big data. IEEE Access, 7 , 139895–139908. https://doi.org/10.1109/ACCESS.2019.2943752

Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53 (8), 5929–5955. https://doi.org/10.1007/s10462-020-09838-1

Vasco, J. C. (2021). Additive manufacturing for the automotive industry. In J. P. Davim & K. Gupta (Eds.), Additive manufacturing (pp. 505–530). Elsevier Inc. https://doi.org/10.1016/B978-0-12-818411-0.00010-0

Chapter   Google Scholar  

Wang, B., Qiu, M., Wang, X., Li, Y., Gong, Y., Zeng, X., et al. (2019). A minimax game for instance based selective transfer learning. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, Anchorage, AK, USA (pp. 34–43). Association for Computing Machinery. https://doi.org/10.1145/3292500.3330841

Wang, J., Li, Y., Gao, R. X., & Zhang, F. (2022a). Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability. Journal of Manufacturing Systems, 63 , 381–391. https://doi.org/10.1016/j.jmsy.2022.04.004

Wang, Z., Yang, W., Liu, Q., Zhao, Y., Liu, P., Wu, D., et al. (2022b). Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions. Journal of Manufacturing Processes, 77 , 13–31. https://doi.org/10.1016/j.jmapro.2022.02.053

Willia, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8 (3), 229–256. https://doi.org/10.1023/A:1022672621406

Xie, Y., Li, B., Wang, C., Zhou, K., Wu, C. T., & Li, S. (2023). A Bayesian regularization network approach to thermal distortion control in 3D printing. Computational Mechanics, 72 (1), 137–154. https://doi.org/10.1007/s00466-023-02270-6

Xie, Y., Li, S., Wu, C. T., Lyu, D., Wang, C., & Zeng, D. (2022). A generalized Bayesian regularization network approach on characterization of geometric defects in lattice structures for topology optimization in preliminary design of 3D printing. Computational Mechanics, 69 (5), 1191–1212. https://doi.org/10.1007/s00466-021-02137-8

Yao, Y., & Doretto, G. (2010). Boosting for transfer learning with multiple sources. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition, San Francisco, CA, USA (pp. 1855–1862). IEEE. https://doi.org/10.1109/CVPR.2010.5539857

Yuan, Y., Chen, Z., Wang, Z., Sun, Y., & Chen, Y. (2023). Attention mechanism-based transfer learning model for day-ahead energy demand forecasting of shopping mall buildings. Energy, 270 , 126878. https://doi.org/10.1016/j.energy.2023.126878

Zhang, K., Xiong, C., Liu, Z., & Liu, Z. (2020). Selective weak supervision for neural information retrieval. In The web conference 2020—proceedings of the world wide web conference, WWW 2020, Taipei, Taiwan, China (pp. 474–485). Association for Computing Machinery. https://doi.org/10.1145/3366423.3380131

Zhang, H., Choi, J. P., Moon, S. K., & Ngo, T. H. (2021). A knowledge transfer framework to support rapid process modeling in aerosol jet printing. Advanced Engineering Informatics, 48 , 101264. https://doi.org/10.1016/j.aei.2021.101264

Zhou, Z., Shen, H., Liu, B., Du, W., & Jin, J. (2021). Thermal field prediction for welding paths in multi-layer gas metal arc welding-based additive manufacturing: A machine learning approach. Journal of Manufacturing Processes, 64 , 960–971. https://doi.org/10.1016/j.jmapro.2021.02.033

Zhu, J., Jiang, Q., Shen, Y., Qian, C., Xu, F., & Zhu, Q. (2022). Application of recurrent neural network to mechanical fault diagnosis: A review. Journal of Mechanical Science and Technology, 36 (2), 527–542. https://doi.org/10.1007/s12206-022-0102-1

Zhu, X., Jiang, F., Guo, C., Xu, D., Wang, Z., & Jiang, G. (2023). Surface morphology inspection for directed energy deposition using small dataset with transfer learning. Journal of Manufacturing Processes, 93 , 101–115. https://doi.org/10.1016/j.jmapro.2023.03.016

Download references

Acknowledgements

The authors gratefully acknowledge funding from the Natural Sciences and Engineering Research Council (NSERC) of Canada [Grant numbers: RGPIN-2019-06601] and the in-kind support of University West (Dr. Morgan Nilsen and Dr. Fredrik Sikström) under the Eureka! SMART project (S0410) titled “TANDEM: Tools for Adaptive and Intelligent Control of Discrete Manufacturing Processes.” Meanwhile, the authors acknowledge the Ph.D. candidate Javid Akhavan at Stevens Institute of Technology, United States, for his help in processing their image datasets.

Author information

Authors and affiliations.

Product Design and Optimization Laboratory, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada

Yifan Tang, Mostafa Rahmani Dehaghani, Pouyan Sajadi & G. Gary Wang

You can also search for this author in PubMed   Google Scholar

Contributions

Yifan Tang: writing—original draft preparation, validation, methodology, investigation, formal analysis, conceptualization. Mostafa Rahmani Dehaghani: resources, methodology, formal analysis, conceptualization. Pouyan Sajadi: resources, methodology, formal analysis, conceptualization. G. Gary Wang: writing—review & editing, supervision, resources, project administration, methodology, funding acquisition, conceptualization.

Corresponding author

Correspondence to G. Gary Wang .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflicts of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Tang, Y., Rahmani Dehaghani, M., Sajadi, P. et al. Selecting subsets of source data for transfer learning with applications in metal additive manufacturing. J Intell Manuf (2024). https://doi.org/10.1007/s10845-024-02402-6

Download citation

Received : 13 November 2023

Accepted : 09 April 2024

Published : 12 May 2024

DOI : https://doi.org/10.1007/s10845-024-02402-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Metal additive manufacturing
• Transfer learning
• Source data selection
• Pareto frontier
• Find a journal
• Publish with us
• Track your research

• Open access
• Published: 12 May 2024

Distance plus attention for binding affinity prediction

• Julia Rahman 1   na1 ,
• M. A. Hakim Newton 2 , 3   na1 ,
• Mohammed Eunus Ali 4 &
• Abdul Sattar 2  

Journal of Cheminformatics volume  16 , Article number:  52 ( 2024 ) Cite this article

Metrics details

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and \(\pi \) -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap.

Scientific Contribution Statement

This study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model’s proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions.

Introduction

Conventional drug discovery, as noted by a recent study [ 1 ], is a resource-intensive and time-consuming process that typically lasts for about 10 to 15 years and costs approximately 2.558 billion USD to bring each new drug successfully to the market. Computational approaches can expedite the drug discovery process by identifying drug molecules or ligands that have high binding affinities towards disease-related proteins and would thus form strong transient bonds to inhibit protein functions [ 2 , 3 , 4 ]. In a typical drug development pipeline, a pool of potential ligands is usually given, and the ligands exhibiting strong binding affinities are identified as the most promising drug candidates against a target protein. In essence, protein-ligand binding affinity values serve as a scoring method to narrow the search space for virtual screening [ 5 ].

Existing computational methods for protein-ligand binding affinity prediction include both traditional machine learning and deep learning-based approaches. Early methods used Kernel Partial Least Squares [ 6 ], Support Vector Regression (SVR) [ 7 ], Random Forest (RF) Regression [ 8 ], and Gradient Boosting [ 9 ]. However, just like various other domains [ 10 , 11 , 12 , 13 , 14 ], drug discovery has also seen significant recent advancements [ 15 , 16 , 17 , 18 ] from the computational power and extensive datasets used in deep learning. Deep learning models for protein-ligand binding affinity prediction take protein-ligand docked complexes as input and give binding affinity values as output. Moreover, these models use various input features to capture the global characteristics of the proteins and the ligands and their local interactions in the pocket areas where the ligands get docked into the proteins.

Recent deep learning models for protein-ligand binding affinity prediction include DeepDTA [ 19 ], Pafnucy [ 20 ], \(K_\text {DEEP}\) [ 21 ], DeepAtom [ 22 ], DeepDTAF [ 23 ], BAPA [ 5 ], SFCNN [ 24 ], DLSSAffinity [ 4 ] EGNA [ 25 ], CAPLA [ 26 ] and ResBiGAAT [ 27 ]. DeepDTA [ 19 ] introduced a Convolutional Neural Network (CNN) model with input features Simplified Molecular Input Line Entry System (SMILES) sequences for ligands and full-length protein sequences. Pafnucy and \(K_{DEEP}\) used a 3D-CNN with 4D tensor representations of the protein-ligand complexes as input features. DeepAtom employed a 3D-CNN to automatically extract binding-related atomic interaction patterns from voxelized complex structures. DeepDTAF combined global contextual features and local binding area-related features with dilated convolution to capture multiscale long-range interactions. BAPA introduced a deep neural network model for affinity prediction, featuring descriptor embeddings and an attention mechanism to capture local structural details. SFCNN employed a 3D-CNN with simplified 4D tensor features having only basic atomic type information. DLSSAffinity employed 1D-CNN with pocket-ligand structural pairs as local features and ligand SMILES and protein sequences as global features. EGNA introduced an empirical graph neural network (GNN) that utilizes graphs to represent proteins, ligands, and their interactions in the pocket areas. CAPLA [ 26 ] utilized a cross-attention mechanism within a CNN along with sequence-level input features for proteins and ligands and structural features for secondary structural elements. ResBiGAAT [ 27 ] integrates a deep Residual Bidirectional Gated Recurrent Unit (Bi-GRU) with two-sided self-attention mechanisms, utilizing both protein and ligand sequence-level features along with their physicochemical properties for efficient prediction of protein-ligand binding affinity.

In this work, we consider the effective capturing of protein-ligand interaction as a key to making further progress in binding affinity prediction. However, as we see from the literature, a sequential feature-based model such as DeepDTA was designed mainly to capture long-range interactions between proteins and ligands, not considering local interactions. CAPLA incorporates cross-attention mechanisms along with sequence-based features to indirectly encompass short-range interactions to some extent. ResBiGAAT employs a residual Bi-GRU architecture and two-sided self-attention mechanisms to capture long-term dependencies between protein and ligand molecules, utilizing SMILES representations, protein sequences, and diverse physicochemical properties for improved binding affinity prediction. On the other hand, structural feature-based models such as Pafnucy, \(K_{DEEP}\) and SFCNN use 3D grids, 4D tensors, or molecular graph representations. These features provide valuable insights into the pocket region of the protein-ligand complexes but incur significant computational costs in terms of memory and processing time. Additionally, these features have limitations in capturing long-range indirect interactions among protein-ligand pairs. DLSSAffinity aims to bridge the gap between short- and long-range interactions by considering both sequential and structural features. Moreover, DLSSAffinity uses 4D tensors for Cartesian coordinates and atom-level features to represent interactions between heavy atoms in the pocket areas of the protein-ligand complexes. These representations of interactions are still indirect, considering the importance of protein-ligand interaction in binding affinity. EGNA tried to use graphs and Boolean-valued adjacency matrices to capture protein-ligand interactions to some extent. However, EGNA’s interaction graph considers only edges between each pair of a \(C_\beta \) atom in the pocket areas of the protein and a heavy atom in the ligand when their distance is below a threshold of \(10\mathring{A}\) .

Inspired by the use of distance measures in protein structure prediction [ 14 , 28 , 29 ], in this work, we employ distance-based input features in protein-ligand binding affinity prediction. To be more specific, we use distances between donor-acceptor [ 30 ], hydrophobic [ 31 , 32 ], and \(\pi \) -stacking [ 31 , 32 ] atoms as interactions between such atoms play crucial roles in protein-ligand binding. These distance measures between various types of atoms could essentially capture more direct and more precise information about protein-ligand interactions than using sequence-based features or various other features representing the pocket areas of the protein-ligand complexes. Moreover, the distance values could more directly capture both short- and long-range interactions than adjacency-based interaction graphs of EGNA or tensor-based pocket area representations of DLSSAffinity. Besides capturing protein-ligand interactions, we also consider only those protein residues with donor, hydrophobic, and \(\pi \) -stacking atoms in this work. Considering only these selective residues is also in contrast with all other methods that use all the protein residues. For ligand representation, we use SMILES strings. After concatenating all input features, we use an attention mechanism to effectively weigh the significance of various input features. Lastly, we enhance the predictive performance of our model by adopting an ensembling approach, averaging the outputs of several trained models.

We name our proposed method as Distance plus Attention for Affinity Prediction (DAAP). On the very well-known CASF-2016 dataset, DAAP significantly outperforms existing methods by achieving the Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. DAAP also shows substantial improvement, ranging from 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap .

In our study, we first demonstrate the robustness of our deep architecture through five-fold cross-validation. Subsequently, the learning curve, as depicted in Fig.  1 , illustrates the dynamics of training and validation loss, providing insights into the stability and reliability of the learning process. Furthermore, we provide a comprehensive performance comparison of our proposed model with current state-of-the-art predictors. We also provide an in-depth analysis of the experimental results. The effectiveness of our proposed features is substantiated through an ablation study and a detailed analysis of input features.

Training and validation loss curve of DAAP

Five-fold cross-validation

This study employs a five-fold cross-validation approach to evaluate the performance of the proposed model thoroughly, demonstrating the robustness of the deep architecture. Table   1 provides the average performance metrics (R, RMSE, MAE, SD, and CI) along with their corresponding standard deviations derived from the 5-fold cross-validation on the CASF \(-\) 2016.290 test set when the model is trained with PDBbind2016 and PDBbind2020 datasets. This presentation highlights the predictor’s predictive accuracy and reliability, emphasising the proposed model’s effectiveness.

Average ensemble

Our proposed approach leverages an attention-based deep learning architecture to predict binding affinity. The input feature set comprises distance matrices, sequence-based features for specific protein residues, and SMILES sequences. To enhance the robustness and mitigate the effects of variability and overfitting, we train five models and employ arithmetic averaging for ensembling. Average ensembling is more suitable than max voting ensembling when dealing with real values.

Table  2 shows the results of five models and their averages when all models have the identical setting of their training parameters and the training datasets. We see that the ensemble results are better than the results of the individual models in both the PDBbind2016 and PDBbind2020 training datasets. To check that the proposed approach is robust over the variability in the training datasets, we also train five models but each with a different training subset. These training subsets were obtained by using sampling with replacement. Table  3 shows the results of these five models and their averages.

Tables  2 and 3 depict that the ensemble results are better than the results of the individual results in both training sets. It might seem counterintuitive to see the average results are better than all the individual results, but note that these are not simple average of averages. When the ensemble results are compared across Tables  2 and 3 , the best results are observed in Table  2 for the PDBbind2020 training set. All evaluation metrics R, RMSE, SD, MAE, and CI display improved performance when using the same training data (Table  2 ) compared to different varying training data (Table  3 ) in PDBbind2020 data set. Accordingly, we choose the ensemble with the same training data for PDBbind2020 (Table  2 ) as our final binding affinity prediction model. Conversely, for PDBbind2016, superior outcomes are obtained from the varied training subsets in Table  3 . Henceforth, the best-performing models using PDBbind2016 and PDBbind2020 will be referred to as DAAP16 and DAAP20, respectively, in subsequent discussions.

Comparison with state-of-the-art methods

In our comparative analysis, we assess the performance of our proposed affinity predictor, DAAP, on the CASF-2016 test set, compared to nine recent state-of-the-art predictors: Pafnucy [ 20 ], DeepDTA [ 19 ], OnionNet [ 3 ], DeepDTAF [ 23 ], SFCNN [ 24 ] DLSSAffinity [ 4 ], EGNA [ 25 ], CAPLA [ 26 ] and ResBiGAAT [ 27 ]. Notably, the most recent predictors have surpassed the performance of the initial four, prompting us to focus our comparison on their reported results. For the latter five predictors, we detail the methodology of obtaining their results as follows:

DLSSAffinity We rely on the results available on DLSSAffinity’s GitHub repository, as direct prediction for specific target proteins is not possible due to the unavailability of its trained model.

SFCNN Utilizing the provided weights and prediction code from SFCNN, we replicate their results, except for CASF-2013. The ambiguity regarding the inclusion of CASF-2013 data in their training set (sourced from the PDBbind database version 2019) leads us to omit these from our comparison.

EGNA We have adopted EGNA’s published results for the CASF-2016 test set with 285 protein-ligand complexes due to differing Uniclust30 database versions for HHM feature construction. We applied EGNA’s code with our HHM features for the other five test sets to ensure a consistent evaluation framework.

CAPLA Predictions are made based on the features given in CAPLA’s GitHub, except for the ADS.74 dataset, where we can’t predict results due to the unavailability of feature sets. Their results are the same as their reported results.

ResBiGAAT We included ResBiGAAT’s published results in our analysis after encountering discrepancies with their online server using the same SMILES sequences and protein sequences from test PDB files as us. Variations in results, particularly for PDB files with multiple chains, led us to rely on their reported data, as it yielded more consistent and higher accuracies than our attempts.

In Table  4 , the first 8 methods, namely Pafnucy, DeepDTA, OnionNet, DeepDTAF, DLSSAffinity, SFCNN, \(EGNA^*\) and CAPLA reported on 290 CASF-2016 protein-ligand complexes. To make a fair comparison with these 8 methods, we compared our proposed method DAAP16 and DAAP20 on those 290 protein-ligand complexes. From the data presented in the Table  4 , it is clear that our DAAP20 approach outperforms all the 8 predictors, achieving the highest R-value of 0.909, the highest CI value of 0.876, the lowest RMSE of 0.987, the lowest MAE of 0.745, and the lowest SD of 0.988. Specifically, compared to the closest state-of-the-art predictor, CAPLA, our approach demonstrated significant improvements, with approximately 5% improvement in R, 12% in RMSE, 14% in MAE, 11% in SD, and 4% in CI metrics, showcasing its superior predictive capabilities. As 3 of the recent predictors, namely SFCNN, EGNA, and ResBiGAAT, reported their result for the 285 protein-ligand complexes on the CASF-2016 dataset, to make a fair comparison with them as well, we assess our predictor, DAAP, on these 285 proteins as well. From the data presented in Table  4 , the results revealed that, across all metrics, DAAP20 outperformed these three predictors on 285 proteins as well. Particularly, compared to the recent predictor ResBiGAAT, our approach demonstrated notable improvements, with around 6% improvement in R, 19% in RMSE, 20% in MAE, and 5% in CI metrics, highlighting its superior predictive capabilities.

Table  5 presents a comprehensive evaluation of the prediction performance of our proposed DAAP approach on five other well-known test sets CASF \(-\) 2013.87, CASF \(-\) 2013.195 ADS.74, CSAR-HiQ.51 and CSAR-HiQ.36. Across these test sets, our DAAP approaches demonstrate superior predictive performance in protein-ligand binding affinity. On the CASF \(-\) 2013.87 dataset, EGNA surpasses CAPLA with higher R-value and CI-value of 0.752 and 0.767, respectively, while CAPLA records lower RMSE, MAE and SD values of 1.512, 1.197, and 1.521. In contrast, our DAAP20 surpasses both, excelling in all metrics with an R of 0.811, RMSE of 1.324, MAE of 1.043, SD of 1.332, and CI of 0.813, with DAAP16 also delivering robust performance. For the CASF \(-\) 2013.195 test set, a similar trend is observed with our DAAP20 approach outperforming the nearest state-of-the-art predictor by a significant margin of 8%-20% across all evaluation metrics. The DAAP16 approach, not DAAP20, stands out on the ADS.74 dataset by surpassing predictors like Pafnucy, SFCNN and EGNA, showcasing substantial improvements of approximately 12%-37% in various metrics. When evaluating the CSAR-HiQ.51 and CSAR-HiQ.36 datasets against six state-of-the-art predictors, DAAP20 consistently outperforms all, indicating enhancements of 2%-20% and 3%-31%, respectively. Although DAAP16 does not surpass ResBiGAAT in CSAR-HiQ.51, it notably excels in the CSAR-HiQ.36 dataset, outperforming ResBiGAAT in all metrics except MAE. These results underscore the exceptional predictive capabilities of our DAAP approach across diverse datasets and evaluation criteria, consistently surpassing existing state-of-the-art predictors.

The distributions of real and predicted binding affinity values by our predictor (green) and the closest state-of-the-art predictor (red) across the six test sets

Figure  2 presents the distributions of actual and predicted binding affinities for our best DAAP approach and the closest state-of-the-art predictor. In all six test sets, a clear linear correlation and low mean absolute error (MAE) between predicted and actual binding affinity values can be observed for our DAAP model, demonstrating the strong performance of our model across these test sets. The other predictors show scattering over larger areas. In our analysis, we could not consider ResBiGAAT in the CSAR-HiQ.51 and CSAR-HiQ.36 datasets due to the unavailability of their results.

Ablation study and explainability

A significant contribution of this work is utilising distance matrix input features to capture critical information about the protein-ligand relationship. Specifically, we employ a concatenation of three distance maps, representing donor-acceptor, hydrophobic, and \(\pi \) -stacking interactions, as input features, effectively conveying essential protein-ligand bonding details. Following finalising our prediction architecture by incorporating two additional features derived from protein and SMILES sequences, we conduct an in-depth analysis of the impact of various combinations of these distance matrices as features. In the case of protein features, residues are selected based on which distance maps are considered.

Table  6 illustrates the outcomes obtained from experimenting with different combinations of distance maps and selected protein residue and ligand SMILES features on the CASF \(-\) 2016.290 test set. We devise four unique combinations, employing three distinct distance maps for both the PDBbind2016 and PDBbind2020 training datasets. Additionally, we explore a combination that integrates donor-acceptor, hydrophobic, and \(\pi \) -stacking distance maps with features from all protein residues, denoted as DA + \(\pi \) S + HP + FP, to evaluate the impact of using all residues versus selected ones.

From the information presented in Table  6 , it is evident that utilizing the donor-acceptor (DA) solely distance maps yields the lowest performance across both training sets, particularly when different combinations of distance maps are paired with selective protein residues. However, as expected, the combination of the three distance maps, namely DA, \(\pi \) S ( \(\pi \) -stacking), and HP (Hydrophobicity), demonstrates superior performance compared to other combinations. Notably, the combination of DA and HP outperforms the other two combinations but falls short of our best-performing feature set. The ensemble of DA, \(\pi \) S, HP and all protein residues exhibit the least favourable outcomes among the tested combinations. This result aligns with our expectations, as Hydrophobic interactions are the most prevalent in protein-ligand binding, underscoring their significance in feature analysis.

Integrating an attention mechanism into our model is crucial in achieving improved results. After consolidating the outputs of three 1D-CNN blocks, we employ attention, each receiving inputs from distance maps, protein sequences, and ligand sequences. The dimension of the feature is 384. As depicted in Fig.  3 , the heatmap visualization highlights the differential attention weights assigned to various features, with brighter and darker regions indicating higher weights to certain features, thus improving binding affinity predictions. This process underscores the mechanism’s ability to discern and elevate critical features, showing that not all features are equally important. Further emphasizing the significance of attention, a comparative analysis using the same model architecture without the attention mechanism on the same features-shown in the last row of Table  6 demonstrates its vital role in boosting predictive accuracy. This comparison not only reinforces the value of the attention mechanism in detecting intricate patterns within the feature space but also significantly enhances the model’s predictive capabilities.

Visualization of attention maps for concatenated features in the 1o0h protein-ligand complex of the CASF \(-\) 2016.290 dataset

Statistical analysis

In assessing the statistical significance of performance differences between DAAP and its closest competitors, Wilcoxon Signed Ranked Tests at a 95% confidence level were conducted. Comparisons included DAAP against CAPLA for CASF \(-\) 2016.290, CASF \(-\) 2013.87, CASF \(-\) 2013.195, CSAR-HiQ.36, and CSAR-HiQ.51 datasets and between DAAP and SFCNN for the ADS.74 test set. Unfortunately, ResBiGAAT’s results were unavailable for inclusion in the analysis. Table  7 depicts that DAAP demonstrated statistical significance compared to the closest state-of-the-art predictor across various test sets, as indicated by p-values ranging from 0.000 to 0.047. The consistently negative mean Z-values, ranging from \(-\) 14.71 to \(-\) 5.086, suggest a systematic improvement in predictive performance. Moreover, higher mean rankings, ranging from 19.5 to 144.5, further emphasize the overall superiority of DAAP. Notably, the superior performance is observed across diverse datasets, including CASF \(-\) 2016.290, CASF \(-\) 2013.87, CASF \(-\) 2013.195, ADS.74, CSAR-HiQ.51, and CSAR-HiQ.36. These findings underscore the robustness and effectiveness of DAAP in predicting protein-ligand binding affinity.

Screening results

In this section, we scrutinize the effectiveness of our predicted affinity scores to accurately differentiate between active binders (actives) and non-binders (decoys) throughout the screening procedure. To this end, we have carefully curated a subset of seven hand-verified targets from the Database of Useful Decoys: Enhanced (DUD-E), accessible via https://dude.docking.org , to serve as our evaluative benchmark. The details about seven targets are given in Table  8 . This table underscores the diversity and challenges inherent in the dataset, reflecting a wide range of D/A ratios that present a comprehensive framework for evaluating the discriminatory power of our predicted affinity scores.

To construct protein-ligand complexes for these targets, we employed AutoDock Vina, configuring the docking grid to a \(20\mathring{A} \times 20\mathring{A} \times 20\mathring{A}\) cube centred on the ligand’s position. This setup and 32 consecutive Monte-Carlo sampling iterations identified the optimal pose for each molecule pair. Our evaluation of the screening performance utilizes two pivotal metrics: the Receiver Operating Characteristic (ROC) curve [ 33 ] and the Enrichment Factor (EF) [ 34 ]. Figure  4 shows the ROC curve and the EF graph for a detailed examination of a predictive model’s efficacy in virtual screening. The ROC curve’s analysis, with AUC values spanning from 0.63 to 0.76 for the seven targets, illustrates our model’s proficient capability in differentiating between actives and decoys. These values, closely approaching the top-left corner of the graph, denote a high true positive rate alongside a low false positive rate, underscoring our model’s efficacy.

Screening Performance of the Predictive Model: Roc curve (left) and EF (right)

Furthermore, the EF graph of Fig.  4 provides a quantitative assessment of the model’s success in prioritizing active compounds within the top fractions of the dataset, notably the top 1% to 10%. Initial EF values ranging from 12.3 to 9.9 for the top 1% underscore our model’s exceptional ability to enrich active compounds beyond random chance significantly. This pronounced enrichment highlights the model’s utility in the early identification of promising candidates. However, the observed gradual decline in EF values with increasing dataset fractions aligns with expectations, reflecting the challenge of sustaining high enrichment levels across broader selections.

Conclusions

In our protein-ligand binding affinity prediction, we introduce atomic-level distance map features encompassing donor-acceptor, hydrophobic, and \(\pi \) -stacking interactions, providing deeper insights into interactions for precise predictions, both for short and long-range. We enhance our model further with specific protein sequence features of specific residues and ligand SMILES information. These features are integrated into an attention-based 1D-CNN architecture that is used a number of times for ensemble-based performance enhancement, resulting in superior results compared to existing methods across six benchmark datasets. Remarkably, on the CASF-2016 dataset, our model achieves a Correlation Coefficient (R) of 0.909, Root Mean Squared Error (RMSE) of 0.987, Mean Absolute Error (MAE) of 0.745, Standard Deviation (SD) of 0.988, and Concordance Index (CI) of 0.876, signifying its potential to advance drug discovery binding affinity prediction. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap .

We describe the protein-ligand dataset used in our work. We also describe our proposed method in terms of its input features, output representations, and deep learning architectures.

Protein-ligand datasets

In the domain of protein-ligand binding affinity research, one of the primary sources for training, validation, and test sets is the widely recognized PDBbind database [ 35 ]. This database is meticulously curated. It comprises experimentally verified protein-ligand complexes. Each complex encompasses the three-dimensional structures of a protein-ligand pair alongside its corresponding binding affinities expressed as \(pK_d\) values. The PDBbind database ( http://www.pdbbind.org.cn/ ) is subdivided into two primary subsets: the general set and the refinement set . The PDBbind version 2016 dataset (named PDBbind2016) contains 9221 and 3685 unique protein-ligand complexes, while the PDBbind version 2020 dataset (named PDBbind2020) includes 14127 and 5316 protein-ligand complexes in the general and refinement sets, respectively.

Similar to the most recent state-of-the-art affinity predictors such as Pafnucy [ 20 ], DeepDTAF [ 23 ], OnionNet [ 3 ], DLSSAffinity [ 4 ], LuEtAl [ 36 ], EGNA [ 25 ] and CAPLA [ 26 ], our DAAP16 method is trained using the 9221 + 3685 = 12906 protein-ligand complexes in the general and refinement subsets of the PDBbind dataset version 2016 . Following the same training-validation set formation approach of the recent predictors such as Pafnucy, OnionNet, DeepDTAF, DLSSAffinity and CAPLA, we put 1000 randomly selected protein-ligand complexes in the validation set and the remaining 11906 distinct protein-ligand pairs in the training set. Another version of DAAP, named DAAP20, was generated using the PDBbind database version 2020 , which aligns with the training set of ResBiGAAT [ 27 ]. To avoid overlap, we filtered out protein-ligand complexes common between the PDBbind2020 training set and the six independent test sets. After this filtering process, 19027 unique protein-ligand complexes were retained for training from the initial pool of 19443 in PDBbind2020.

To ensure a rigorous and impartial assessment of the effectiveness of our proposed approach, we employ six well-established, independent blind test datasets. There is no overlap of protein-ligand complexes between the training sets and these six independent test sets.

CASF-2016.290 The 290 protein-ligand complexes, commonly referred to as CASF-2016, are selected from the PDBbind version 2016 core set ( http://www.pdbbind.org.cn/casf.php ) and have become the gold standard test set for recent affinity predictors such as DLSSAffinity [ 4 ], LuEtAl [ 36 ], EGNA [ 25 ] and CAPLA [ 26 ].

CASF-2013.87 and CASF-2013.195 Similar to the approach taken by DLSSAffinity [ 4 ], we carefully curated 87 unique protein-ligand complexes from the CASF-2013 dataset, which originally consists of 195 complexes ( http://www.pdbbind.org.cn/casf.php ). These 87 complexes were chosen to ensure no overlap with our training set or the CASF-2016 test set. Additionally, we use the entire set of 195 complexes as another test set, named CASF \(-\) 2013.195.

ADS.74 This test set from SFCNN [ 24 ] comprises 74 protein-ligand complexes sourced from the Astex diverse set [ 37 ].

CSAR-HiQ.51 and CSAR-HiQ.36 These two test datasets contain 51 and 36 protein-ligand complexes from the well-known CSAR [ 38 ] dataset. Recent affinity predictors such as EGNA [ 25 ], CAPLA and ResBiGAAT [ 26 , 27 ] have employed CSAR as a benchmark dataset. To get our two test datasets, we have followed the procedure of CAPLA and filtered out protein-ligand complexes with duplicate PDB IDs from two distinct CSAR subsets containing 176 and 167 protein-ligand complexes, respectively.

Input features

Given protein-ligand complexes in the datasets, we extract three distinctive features from proteins, ligands, and protein-ligand binding pockets. We describe these below.

Protein representation

We employ three distinct features for encoding protein sequences: one-hot encoding of amino acids, a Hidden Markov model based on multiple sequence alignment features (HHM), and seven physicochemical properties.

In the one-hot encoding scheme for the 20 standard amino acids and non-standard amino acids, each amino acid is represented by a 21-dimensional vector. This vector contains twenty “0 s” and one “1”, where the position of the “1” corresponds to the amino acid index in the protein sequence.

To construct the HHM features, we have run an iterative searching tool named HHblits [ 39 ] against the Uniclust30 database ( http://wwwuser.gwdg.de/~compbiol/uniclust/2020_06/ ) as of June 2020. This process allows us to generate HHM sequence profile features for the proteins in our analysis. Each resulting .hhm feature file contains 30 columns corresponding to various parameters such as emission frequencies, transition frequencies, and Multiple Sequence Alignment (MSA) diversities for each residue. Like EGNA, for columns 1 to 27, the numbers are transformed into frequencies using the formula \(f = 2^{-0.001*p}\) , where f represents the frequency, and p is the pseudo-count. This transformation allows the conversion of these parameters into frequency values. Columns 28 to 30 are normalized using the equation: \(f = \frac{0.001*p}{20}\) . This normalization process ensures that these columns are appropriately scaled for further analysis and interpretation.

The seven physicochemical properties [ 14 , 29 ] for each amino acid residue are steric parameter (graph shape index), hydrophobicity, volume, polarisability, isoelectric point, helix probability, and sheet probability. When extracting these three features for protein residues, we focused exclusively on the 20 standard amino acid residues. If a residue is non-standard, we assigned a feature value of 0.0.

In our approach, we initially concatenate all three features sequentially for the entire protein sequence. Subsequently, to enhance the specificity of our model, we employ a filtering strategy where residues lacking donor [ 40 ], hydrophobic [ 31 ], and \(\pi \) -stacking [ 32 ] atoms within their amino acid side chains are excluded from the analysis. Additionally, to prevent overlap, we select unique residues after identification based on donor, hydrophobic, or \(\pi \) -stacking atoms for each protein sequence. The rationale behind this filtering is to focus on residues that are actively involved in critical interactions relevant to protein-ligand binding. The resulting feature dimension for each retained protein residue is 58. This feature set includes one-hot encoding of amino acids, a Hidden Markov model based on multiple sequence alignment features (HHM), and seven physicochemical properties. These features are comprehensively summarised in Table  9 for clarity.

Considering the variable numbers of residues that proteins can possess, we have considered a standardized protein sequence length to align with the fixed-size requirements of deep learning algorithms. In our initial experiments exploring various sequence lengths in the datasets, we found that a maximum length of 500 yields better performance in terms of pearson correlation coefficient (R) and mean absolute error (MAE). If the number of selected residues falls below 500, we pad the sequence with zeros; conversely, if it exceeds 500, we truncate it to 500 from the initial position of the sequence. The final dimension of each protein is \(500\times 58\) .

Ligand representation

We use SMILES to represent ligands. SMILES is a widely adopted one-dimensional representation of chemical structures of ligands [ 41 ]. To convert ligand properties such as atoms, bonds, and rings from ligand SDF files into SMILES strings, we use the Open Babel chemical tool [ 42 ]. The SMILES strings comprise 64 unique characters, each corresponding to a specific numeric digit ranging from 1 to 64. For example, the SMILES string “HC(O=)N” is represented as [12, 42, 1, 48, 40, 31, 14]. In line with our protein representation approach, we set a fixed length of 150 characters for each SMILES string.

Various distance measures that potentially capture protein-ligand interactions. In the figure, \(d_{ij}\) represents the distance between a donor (D), hydrophobic (H), or \(\pi \) -stacking (S) atom i in the protein and the corresponding acceptor (A), hydrophobic (H), or \(\pi \) -stacking (S) atom j in the ligand. Empty circles represent other atom types. Different colour lines represent different types of interactions

Binding pocket representation

A binding pocket refers to a cavity located either on the surface or within the interior of a protein. A binding pocket possesses specific characteristics that make it suitable for binding a ligand [ 43 ]. Protein residues within the binding pocket region exert a direct influence, while residues outside this binding site can also have a far-reaching impact on affinity prediction. Among various protein-ligand interactions within the binding pocket regions, donor-acceptor atoms [ 30 ], hydrophobic contacts [ 31 , 32 ], and \(\pi \) -stacking [ 31 , 32 ] interactions are the most prevalent, and these interactions could significantly contribute to the enhancement of affinity score prediction. The formation of the protein-ligand complexes involves donor atoms from the proteins and acceptor atoms from the ligands. This process is subject to stringent chemical and geometric constraints associated with protein donor groups and ligand acceptors [ 30 ]. Hydrophobic interactions stand out as the primary driving force in protein-ligand interactions, while \(\pi \) -stacking interactions, particularly involving aromatic rings, play a substantial role in protein-ligand interactions [ 32 ]. However, there are instances where donor-acceptor interactions alone may not suffice, potentially failing to capture other interactions that do not conform to traditional donor-acceptor patterns. In such scenarios, hydrophobic contacts and \(\pi \) -stacking interactions become essential as they could provide valuable insights for accurate affinity prediction.

We employ three types of distance matrices in our work shown in Fig.  5 to capture protein-ligand interactions. The first one is the donor-acceptor distance matrix , which considers distances between protein donor atoms and acceptor ligand atoms, with data sourced from mol2/SDF files. We ensure that all ligand atoms contribute to the distance matrix construction, even in cases where ligands lack explicit acceptor atoms. Furthermore, we calculate the hydrophobic distance matrix by measuring the distance between hydrophobic protein atoms and hydrophobic ligand atoms, ensuring the distance is less than \(4.5\mathring{A}\) [ 31 ]. Similarly, we compute the \(\pi \) - stacking distance matrix by considering protein and ligand \(\pi \) -stacking atoms and applying a distance threshold of \(4.0\mathring{A}\) [ 32 ]. These three types of atoms are selected from the heavy atoms, referring to any atom that is not hydrogen.

We discretize the initially calculated real-valued distance matrices representing the three types of interactions into binned distance matrices. These matrices are constrained within a maximum distance threshold of \(20\mathring{A}\) . The decision to set a maximum distance threshold of \(20\mathring{A}\) for capturing the binding pocket’s spatial context is informed by practices in both affinity prediction and protein structure prediction fields. Notably, methodologies like Pafnucy [ 20 ], DLSSAffinity [ 4 ], and EGNA [ 25 ], as well as advanced protein structure prediction models such as AlphaFold [ 28 ] and trRosetta [ 44 ], utilize a 20Å range to define interaction spaces or predict structures. This consensus on the 20Å threshold reflects its sufficiency in providing valuable spatial information necessary for accurate modeling. The distance values ranging from \(0\mathring{A} - 20\mathring{A}\) are discretized into 40 bins, each with a \(0.5\mathring{A}\) interval. Any distance exceeding \(20\mathring{A}\) is assigned to the \(41^{st}\) bin. In our experimentation, we explored different distance ranges ( \(20\mathring{A}\) , \(25\mathring{A}\) , \(30\mathring{A}\) , \(35\mathring{A}\) , and \(40\mathring{A}\) ) while maintaining a uniform bin interval of \(0.5\mathring{A}\) . Among these ranges, \(20\mathring{A}\) yielded optimal results, and as such, we adopted it for our final analysis. Following this binning process, the original real-valued distances in the matrices are substituted with their corresponding bin numbers. Subsequently, we convert the 2D distance matrix into a 1D feature vector. We concatenate the three 1D vectors representing the three distinct interactions into a single vector to construct the final feature vector. To ensure consistency, the maximum length of the feature vector is set to 1000 for each pocket.

Output representations

This binding affinity is measured in the dissociation constant ( \(K_d\) ). For simplicity in calculations, the actual affinity score \(K_d\) is commonly converted into \(pK_d\) by taking the negative logarithm of \(K_d\) .

Deep learning architectures

The proposed model architecture

We propose a deep-learning regression model to predict protein-ligand binding affinities, shown in Fig.  6 . Our model comprises three integral components: convolutional neural network (CNN), attention mechanism, and fully connected neural network (FCNN). Before feeding to the CNN block, information from three distinct feature sources (proteins, ligands, and interactions) is encoded and subsequently processed through the embedding layer. The embedding layer transforms the inputs into fixed-length vectors of a predefined size (in this case, 128 dimensions), enabling more effective feature representation with reduced dimensionality. During training, our model operates with a batch size of 16 and is optimized using the Adam optimizer and a learning rate set at 0.001. We adopt the log cosh loss function for this work to optimise the model’s performance. The training regimen consists of 200 epochs, with the best model selected based on the validation loss, and a dropout rate of 0.2 is applied. The explored hyperparameter settings are summarised in Table  10 . We have explored these settings, and after preliminary experiments, we have selected these values which are emboldened.

Convolutional neural network

Much like DLSSAffinity [ 4 ], our model employs three 1D-CNN blocks, each dedicated to processing distinct feature sources: proteins, ligands, and interactions in pockets. Each of these 1D-CNN blocks comprises three convolutional layers paired with three Maxpooling layers. The configuration of the first two 1D-CNN blocks includes 32, 64, and 128 filters, each with corresponding filter lengths of 4, 8, and 12. In contrast, the 1D-CNN block responsible for handling SMILES sequence inputs features filters with 4, 6, and 8 adjusted lengths. Each of the three 1D-CNN blocks in our model generates a 128-dimensional output. Subsequently, before progressing to the next stage, the outputs of these three 1D-CNN blocks are concatenated and condensed into a unified 384-dimensional output.

Attention mechanism

In affinity prediction, attention mechanisms serve as crucial components in neural networks, enabling models to allocate varying levels of focus to distinct facets of input data [ 5 ]. These mechanisms play a critical role in weighing the significance of different features or entities when assessing their interaction strength. The attention mechanism uses the formula below.

We use the Scaled Dot-Product Attention [ 45 ] mechanism to calculate and apply attention scores to the input data. The attention mechanism calculates query ( Q ), key ( K ), and value ( V ) matrices from the input data. In this context, Q is a vector capturing a specific aspect of the input, K represents the context or memory of the model with each key associated with a value, and V signifies the values linked to the keys. It computes attention scores using the dot product of Q and K matrices, scaled by the square root of the dimensionality ( \(d_k\) ). Subsequently, a softmax function normalises the attention scores. Finally, the output is generated as a weighted summation of the value (V) matrix, guided by the computed attention scores.

Notably, the output of the concatenation layer passes through the attention layer. The input to the attention layer originates from the output of the concatenation layer, preserving the same dimensionality as the input data. This design ensures the retention of crucial structural information throughout the attention mechanism.

Fully connected neural network

The output of the attention layer transitions into the subsequent stage within our model architecture, known as the Fully Connected Neural Network (FCNN) block. The FCNN block consists of two fully connected (FC) layers, where the two layers have 256 and 128 nodes respectively. The final stage in our proposed prediction model is the output layer, which follows the last FC layer.

Evaluation metrics

We comprehensively evaluate our affinity prediction model using five well-established performance metrics. The Pearson Correlation Coefficient (R) [ 4 , 24 , 26 , 36 ] measures the linear relationship between predicted and actual values. The Root Mean Square Error (RMSE) [ 4 , 24 , 26 ] and the Mean Absolute Error (MAE) [ 24 , 26 ] assess prediction accuracy and error dispersion. The Standard Deviation (SD) [ 4 , 24 , 26 , 36 ] evaluates prediction consistency, and the Concordance Index (CI) [ 26 , 36 ] determines the model’s ability to rank protein-ligand complexes accurately. Higher R and CI values and lower RMSE, MAE, and SD values indicate better prediction accuracy. These metrics are collectively very robust measures for comparison of our model’s performance against that of the state-of-the-art techniques in the field of affinity prediction.

N : the number of protein-ligand complexes

\(Y_{\text {act}}\) : experimentally measured actual binding affinity values for the protein-ligand complexes

\(Y_{\text {pred}}\) : the predicted binding affinity values for the given protein-ligand complexes

\(y_{\text {act}_i}\) and \(y_{\text {pred}_i}\) : respectively the actual and predicted binding affinity value of the \(i^{th}\) protein-ligand complex

a : is slope

b : interpretation of the linear regression line of the predicted and actual values. Z : the normalization constant, i.e. the number of data pairs with different label values.

h ( u ): the step function that returns 1.0, 0.5, and 0.0for \(u>0\) , \(u = 0\) , and \(u<0\) respectively.

Availability of data and materials

The program and corresponding data are publicly available on the website https://gitlab.com/mahnewton/daap .

DiMasi JA, Grabowski HG, Hansen RW (2016) Innovation in the pharmaceutical industry: new estimates of r &d costs. J Health Econ 47:20–33

Article   PubMed   Google Scholar  

Gilson MK, Zhou H-X (2007) Calculation of protein-ligand binding affinities. Ann Rev Biophys Biomol Str 36(1):21–42

Article   CAS   Google Scholar  

Zheng L, Fan J, Mu Y (2019) Onionnet: a multiple-layer intermolecular-contact-based convolutional neural network for protein-ligand binding affinity prediction. ACS Omega 4(14):15956–15965

Article   CAS   PubMed   PubMed Central   Google Scholar  

Wang H, Liu H, Ning S, Zeng C, Zhao Y (2022) Dlssaffinity: protein-ligand binding affinity prediction via a deep learning model. Phys Chem Chem Phys 24(17):10124–10133

Article   CAS   PubMed   Google Scholar  

Seo S, Choi J, Park S, Ahn J (2021) Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions. BMC Bioinform 22(1):1–15

Article   Google Scholar  

Deng W, Breneman C, Embrechts MJ (2004) Predicting protein- ligand binding affinities using novel geometrical descriptors and machine-learning methods. J Chem Inf Comput Sci 44(2):699–703

Li L, Wang B, Meroueh SO (2011) Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries. J Chem Inf Modeling 51(9):2132–2138

Ballester PJ, Mitchell JB (2010) A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26(9):1169–1175

Li H, Peng J, Sidorov P, Leung Y, Leung K-S, Wong M-H, Lu G, Ballester PJ (2019) Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data. Bioinformatics 35(20):3989–3995

Deng L, Platt J. Ensemble deep learning for speech recognition. In: Proc. Interspeech. 2014

Chen C, Seff A, Kornhauser A, Xiao J. Deepdriving: learning affordance for direct perception in autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, 2015; pp. 2722–2730

Lin T-Y, RoyChowdhury A, Maji S (2017) Bilinear convolutional neural networks for fine-grained visual recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1309–1322

Newton MH, Rahman J, Zaman R, Sattar A. Enhancing protein contact map prediction accuracy via ensembles of inter-residue distance predictors. Computational Biology and Chemistry, 2022; 107700.

Rahman J, Newton MH, Hasan MAM, Sattar A (2022) A stacked meta-ensemble for protein inter-residue distance prediction. Comput Biol Med 148:105824

Isert C, Atz K, Schneider G (2023) Structure-based drug design with geometric deep learning. Curr Opin Struct Biol 79:102548

Krentzel D, Shorte SL, Zimmer C (2023) Deep learning in image-based phenotypic drug discovery. Trend Cell Biol 33(7):538–554

Yang L, Jin C, Yang G, Bing Z, Huang L, Niu Y, Yang L (2023) Transformer-based deep learning method for optimizing admet properties of lead compounds. Phys Chem Chem Phys 25(3):2377–2385

Masters MR, Mahmoud AH, Wei Y, Lill MA (2023) Deep learning model for efficient protein-ligand docking with implicit side-chain flexibility. J Chem Inf Modeling 63(6):1695–1707

Öztürk H, Özgür A, Ozkirimli E (2018) Deepdta: deep drug-target binding affinity prediction. Bioinformatics 34(17):821–829

Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P (2018) Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics 34(21):3666–3674

Jiménez J, Skalic M, Martinez-Rosell G, De Fabritiis G (2018) \(k_{deep}\) : protein-ligand absolute binding affinity prediction via 3d-convolutional neural networks. J Chem Inf Modeling 58(2):287–296

Li Y, Rezaei MA, Li C, Li X (2019) Deepatom: a framework for protein-ligand binding affinity prediction. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 303–310, IEEE

Wang K, Zhou R, Li Y, Li M (2021) Deepdtaf: a deep learning method to predict protein-ligand binding affinity. Brief Bioinf 22(5):072

Wang Y, Wei Z, Xi L (2022) Sfcnn: a novel scoring function based on 3d convolutional neural network for accurate and stable protein-ligand affinity prediction. BMC Bioinform 23(1):1–18

Xia C, Feng S-H, Xia Y, Pan X, Shen H-B (2023) Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network. Brief Bioinf. https://doi.org/10.1093/bib/bbac603

Jin Z, Wu T, Chen T, Pan D, Wang X, Xie J, Quan L, Lyu Q (2023) Capla: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism. Bioinformatics 39(2):049

Abdelkader GA, Njimbouom SN, Oh T-J, Kim J-D (2023) Resbigaat: Residual bi-gru with attention for protein-ligand binding affinity prediction. Computational Biology and Chemistry, 107969

Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson AW, Bridgland A et al (2020) Improved protein structure prediction using potentials from deep learning. Nature 577(7792):706–710

Rahman J, Newton MH, Islam MKB, Sattar A (2022) Enhancing protein inter-residue real distance prediction by scrutinising deep learning models. Sci Rep 12(1):787

Raschka S, Wolf AJ, Bemister-Buffington J, Kuhn LA (2018) Protein-ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes. J Computer-aided Mol Design 32:511–528

Jubb HC, Higueruelo AP, Ochoa-Montaño B, Pitt WR, Ascher DB, Blundell TL (2017) Arpeggio: a web server for calculating and visualising interatomic interactions in protein structures. J Mol Biol 429(3):365–371

Freitas RF, Schapira M (2017) A systematic analysis of atomic protein-ligand interactions in the pdb. Medchemcomm 8(10):1970–1981

Empereur-Mot C, Guillemain H, Latouche A, Zagury J-F, Viallon V, Montes M (2015) Predictiveness curves in virtual screening. J Cheminf 7(1):1–17

Li H, Zhang H, Zheng M, Luo J, Kang L, Liu X, Wang X, Jiang H (2009) An effective docking strategy for virtual screening based on multi-objective optimization algorithm. BMC Bioinf 10:1–12

Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) Bindingdb: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35(suppl–1):198–201

Lu Y, Liu J, Jiang T, Guan S, Wu H. Protein-ligand binding affinity prediction based on deep learning. In: International Conference on Intelligent Computing, 2022; pp. 310–316. Springer.

Hartshorn MJ, Verdonk ML, Chessari G, Brewerton SC, Mooij WT, Mortenson PN, Murray CW (2007) Diverse, high-quality test set for the validation of protein- ligand docking performance. J Med Chem 50(4):726–741

Dunbar JB Jr, Smith RD, Yang C-Y, Ung PM-U, Lexa KW, Khazanov NA, Stuckey JA, Wang S, Carlson HA (2011) Csar benchmark exercise of 2010: selection of the protein-ligand complexes. J Chem Inf Modeling 51(9):2036–2046

Remmert M, Biegert A, Hauser A, Söding J (2012) Hhblits: lightning-fast iterative protein sequence searching by hmm-hmm alignment. Nat Methods 9(2):173–175

Hydrogen donor and acceptor atoms of the amino acid. https://www.imgt.org/IMGTeducation/Aide-memoire/_UK/aminoacids/charge/ . Accessed: 13-08-2023

Weininger D (1988) Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. J Chem Inf Comput Sci 28(1):31–36

O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: An open chemical toolbox. J Cheminf 3(1):1–14

Google Scholar  

Stank A, Kokh DB, Fuller JC, Wade RC (2016) Protein binding pocket dynamics. Accounts Chem Res 49(5):809–815

Yang J, Anishchenko I, Park H, Peng Z, Ovchinnikov S, Baker D (2020) Improved protein structure prediction using predicted interresidue orientations. Proc Natl Acad Sci 117(3):1496–1503

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30

Download references

Acknowledgements

This research is partially supported by the research seed grant awarded to M.A.H.N. at the University of Newcastle. The research team acknowledges the valuable assistance of the Griffith University eResearch Service & Specialised Platforms team for granting access to their High-Performance Computing Cluster, which played a crucial role in completing this research endeavour.

This research is partially supported by the research seed Grant awarded to M.A.H.N. at the University of Newcastle.

Author information

Julia Rahman and M. A. Hakim Newton are co-first-authors and contributed equally.

Authors and Affiliations

School of Information and Communication Technology, Griffith University, 170 Kessels Rd, Nathan, 4111, QLD, Australia

Julia Rahman

Institute for Integrated and Intelligent Systems (IIIS), Griffith University, 170 Kessels Rd, Nathan, 4111, QLD, Australia

M. A. Hakim Newton & Abdul Sattar

School of Information and Physical Sciences, University of Newcastle, University Dr, Callaghan, 2308, NSW, Australia

M. A. Hakim Newton

Department of Computer Science & Engineering, Bangladesh University of Engineering and Technology, Palashi, 1205, Dhaka, Bangladesh

Mohammed Eunus Ali

You can also search for this author in PubMed   Google Scholar

Contributions

The contributions of the authors to this work were as follows: J.R. and M.A.H.N. played equal roles in all aspects of the project, including conceptualization, data curation, formal analysis, methodology, software development, and writing of the initial draft. M.E.A. helped in the concept development, review and editing of the manuscript. A.S. actively engaged in discussions, facilitated funding acquisition, provided supervision, and thoroughly reviewed the manuscript.

Corresponding author

Correspondence to Julia Rahman .

Ethics declarations

Competing interests.

No Conflict of interest is declared.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Rahman, J., Newton, M.A.H., Ali, M.E. et al. Distance plus attention for binding affinity prediction. J Cheminform 16 , 52 (2024). https://doi.org/10.1186/s13321-024-00844-x

Download citation

Received : 01 December 2023

Accepted : 24 April 2024

Published : 12 May 2024

DOI : https://doi.org/10.1186/s13321-024-00844-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Binding affinity
• Distance matrix
• Donor-acceptor
• Hydrophobicity
• \(\pi \) -Stacking
• Deep learning

Journal of Cheminformatics

ISSN: 1758-2946

• Submission enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 14 May 2024

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure

• Junbo Chen 1   na1 ,
• Vara Lakshmi Bayanagari 1   na1 ,
• Sohae Chung 2 , 3 ,
• Yao Wang 1 , 4 &
• Yvonne W. Lui 2 , 3  

Scientific Reports volume  14 , Article number:  9835 ( 2024 ) Cite this article

Metrics details

• Diffusion tensor imaging
• Magnetic resonance imaging

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22–37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92–0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.

Introduction

Biological sex (throughout this manuscript, sex, male and female refer to biological sex assigned at birth) is a crucial variable in neuroscience research. The National Institutes of Health require all preclinical and human subject studies to account for biological variables including sex in the research plan. Understanding of sex differences is particularly important as it has been shown to relate to a wide range of cognitive functions such as motor cognitive performance 1 , 2 , 3 , nonverbal reasoning 3 , working memory 4 , 5 , 6 and episodic memory 7 , 8 , 9 . In addition, prevalence of several neurological and neuropsychiatric disorders differs between males and females. For example, autism spectrum disorder and Tourette syndrome are more prevalent in males 10 , 11 , while disorders such as multiple sclerosis and depression are more prevalent in females 12 , 13 .

Recent advances in MRI have enabled precise measurement of the brain noninvasively. However, most MRI studies have documented structural differences between sexes in terms of gross brain volume 14 , 15 and cortical thickness 16 though inconsistencies exist between reports throughout individuals 17 , 18 , 19 . Perhaps of greater interest is whether there exist differences in cellular-level organization of the brain between males and females. Better understanding underlying sex differences in brain microstructure would inform how biological sex influences brain health and disease. Indeed, cellular-level microstructure is known to be informative in various brain studies including brain development, aging, and neurological diseases such as demyelination, tumor infiltration, and dementia 20 , 21 , 22 , 23 . Some studies have been conducted on ex vivo samples from animal models that look at cellular features such as density of microglia 24 , 25 ; however, a true picture of the brain’s cellular structure in vivo remains elusive and ex vivo studies are limited by fixation and preparation artifacts which invariably alter the cellular matrix.

Diffusion MRI is capable of capturing microscopic features of the brain noninvasively 26 and is actively being used to study various neurological diseases, ranging from neurodegenerative disorders such as Alzheimer’s dementia 27 , 28 and Parkinson’s disease 29 to autoimmune disorders such as Multiple Sclerosis 30 . Previous studies have found evidence to support the sex differences in diffusion parameters. For example, in different age ranges, analyses have indicated sex differences in various diffusion parameters such as fractional anisotropy (FA) 16 , 31 , 32 , 34 , 35 , 62 , 63 , orientation dispersion 16 , 62 , 63 , mean diffusivity (MD) 32 , 35 , 63 , axial diffusivity (AD) and radial diffusivity (RD) 32 , 63 . The sex differences have been indicated in various white matter regions such as thalamic radiation 16 , 32 , cerebellar 31 , superior longitudinal fasciculus 31 , 32 , corpus callosum 31 , 32 , 63 , corona radiata 32 . However, of the studies that have been conducted, most rely on conventional statistical analysis methods in various regions-of-interest 16 , 31 , 32 , 62 , 63 . The results have been criticized as rigorous correction for multiple comparisons seems to diminish the power of such differences, which have raised debate and the need for new approaches to study the sex differences in brain 33 .

Recent advances in deep neural networks provide advanced methods to capture sex differences in brain microstructure. Two recent works using neural networks with handcrafted features from structural connectivity indicated sex-related differences 34 , 35 ; however, the use of complex hand-crafted features has again been criticized by some as cumbersome, adding potential biases, and limited in reproducibility. Besides, as different deep neural networks architectures are highly likely biased toward different types of features 46 , 47 , it is challenging for studies based on a single model or similar models to capture thorough information. Therefore, inclusion of multiple distinct model architectures is necessary.

In this work, we have designed a comprehensive, rigorous learning-based approach aimed at contributing new evidence and insights of sex differences to the debate on whether there are indeed sex-related differences in the human brain microstructure. We hypothesize that sex differences exist in brain microstructure as various types of features, such as local features and global interactions. We accomplish this by leveraging end-to-end deep neural networks and diffusion MRI able to tap in vivo brain microstructure. The end-to-end design obviates the need for complex, a priori choices for either choosing of ROIs or hand-crafted feature engineering that could bias analysis. We register all subjects’ brains to a standard template space to remove the potential influence of overall brain size and volume. In addition, we explore 3 major, popular network architectures that capture different and complementary information and thus this work does not rely on a single model choice or model type. Finally, we attempt to identify WM areas that contribute most significantly to sex classification, and thereby have the most embedded sex-related differences. Instead of building a sex classifier, the goal of this work is to provide new evidence and insights regarding sex-related differences in brain tissue microstructure.

Materials and methods

Study population.

The study includes 1031 healthy adult subjects (age range, 22–37 years) from the Human Connectome Project (HCP—Young dataset) 36 , whereby sex labels were collected through self-reporting and no subject was found to have different self-reported sex from genetic sex. Institutional review board approval and participants’ informed consent were obtained at the participating institutions. Demographic details are summarized in Table 1 .

Diffusion MRI

Diffusion MR images were collected on a 3T scanner (Connectome Skyra, Siemens Medical Solutions, Erlangen, Germany) and preprocessed as per HCP protocol 36 , 37 . In brief, diffusion imaging was performed with the following parameters: 3 b-values (1000, 2000, 3000 s/mm 2 ), 90 diffusion orientations per shell, 18 b 0 (b-value = 0) images, 1.25 mm isotropic image resolution, field of view = 210 mm, number of slices = 111, TR/TE = 5520/89.5 ms, each scan was repeated along 2 phase encoding directions (RL/LR), details can be found in HCP dataset 36 . The diffusion data was preprocessed by HCP for correction of artifacts like motion and eddy-currents artifacts 37 . We use a in-house image processing tool to generate diffusion metrics 38 , including fractional anisotropy (FA), mean diffusivity (MD) and mean kurtosis (MK) to assess white matter microstructure. FA and MD are included because they are the two most commonly used diffusion metrics for characterization of tissue microstructure in many studies 39 . Of note, FA measures directionality of water movement in brain tissue, known to be sensitive to microstructures such as axons and myelin 40 ; and MD measures mean water diffusivity, sensitive to characteristics like cellularity 41 . Here, we also include MK derived from diffusion kurtosis imaging (DKI) to compactly represent non-Gaussian behavior of water molecules as a measure of tissue complexity 42 . All metrics are registered to the FA template in the MNI space 43 using FMRIB Software Library (FSL) 44 so as to remove effects of any macroscopic anatomical differences such as size and contour of the brain itself.

End-to-end classification models

This study employs three major model architectures: 2D convolutional neural network (CNN) 50 , 3D CNN 53 , 54 , 55 , and 3D vision transformer (ViT) 47 , 58 . We choose these end-to-end deep networks that act on the entire image volume to avoid any reliance on hand-crafted features and/or complicated feature engineering. In general, CNN and ViT show state-of-the-art performances broadly across image classification tasks. The two architectures have their own strengths and may be complementary: CNN has inductive bias by design such as locality and translation equivalence/invariance (with/without pooling), making such a model generally more sample-efficient and easier in theory to capture local features of an image or volume 45 . While ViT lacks the inductive bias from convolutional layers rendering them somewhat more data-hungry, ViT has strengths that CNNs lack in being able to capture long-range interactions and more global features present in an image or imaging volume 46 , 47 , which could be important for capturing potential sex differences exist as long-range interactions. Although 3D CNN may be an intuitive choice of architecture to handle a 3D imaging volume, a 3D CNN requires more parameters and more training samples compared with a 2D CNN. Thus, we also test the performance of a 2D CNN with a lighter feature extraction backbone and greater training efficiency.

2D convolutional neural network

In this work, we use a ResNet18 50 as a 2D CNN backbone for feature extraction. Here, the 2D network essentially receives input from a small 3-slice subvolume as ResNet18 is designed to receive color images with 3 channels (RGB). We extract features from every 3 consecutive slices and combine features from all non-overlapping 3-slice subvolumes for the prediction head for classification (Fig.  1 ). Specifically, given input volumetric data with the shape of \(S\hspace{0.17em}\times \hspace{0.17em}H\hspace{0.17em}\times \hspace{0.17em}W\) (S: slice number, H × W: slice size, with each slice in sagittal view), we generate \(S\) /3 2D 3-channel images each with the shape of \(3\hspace{0.17em}\times \hspace{0.17em}H\hspace{0.17em}\times \hspace{0.17em}W\) . The same ResNet18 is applied to extract features from each 3-slice subvolume and features from all \(S\) /3 3-slice subvolumes are concatenated as the input to a linear prediction head. The ResNet18 architecture is shown at the bottom of Fig.  1 . The input is fed to a convolutional layer (conv layer) (kernel-size = 7 × 7, stride = 2, channel-number or number-of-feature-maps = 64), followed by a max-pooling layer for further downsampling (kernel-size = 3 × 3, stride = 2). After the pooling, 8 convolutional layer blocks called residual blocks (where input to the block is added to the output via residual short-cut connection) are applied where each block contains 2 convolutional layers with kernel-size = 3 × 3, the channel number gets doubled and the spatial size gets downsampled by 2 at the first conv layers of 3rd, 5th, 7th residual blocks. Each conv layer is followed by batch-normalization 51 and ReLU activation 52 . In the end of ResNet18, global-average pooling is applied to each feature map to generate a single feature value, leading to 512 features for each 3-slice subvolume. Given \(SxHxW\) =180 × 224 × 224, we have \(S\) /3 = 60 3-slice subvolumes with each yielding 512 features. These 60 × 512 features are concatenated and fed to a linear layer for final prediction, which is a fully-connected layer mapping 60 × 512 features to the predicted class score.

Our 2D CNN model. In the top of the figure, the imaging volume is divided into subvolumes, and a shared ResNet18 is applied to extract 512 features from each subvolume. The features are concatenated and fed to a linear layer for the final prediction. The bottom of the figure shows the architecture of ResNet18 (residual connection, ReLU activation, batch normalization are omitted for simplicity): The input is first fed into a convolutional layer (7 × 7 kernel-size, stride = 2, channel-number = 64) followed by a max-pooling (kernel-size = 3 × 3, stride = 2) layer; subsequently, 8 residual blocks are applied with each containing 2 convolutional layers. Residual blocks parameters: conv layers in block 1, 2 have kernel-size = 3 × 3 and channel = 64; conv layers in block 3, 4 have kernel-size = 3 × 3 and channel = 128; conv layers in block 5, 6 have kernel-size = 3 × 3 and channel = 256; conv layers in block 7, 8 have kernel-size = 3 × 3 and channel = 512; stride = 2 is applied at the first conv layer of block 3, 5, 7. Global average pooling is applied at the end. The classification head is made with one fully-connected layer.

3D convolutional neural network

We employ 3D ResNet-10 53 , 54 , 55 as our 3D CNN backbone, with architecture shown in Fig.  2 . The 3D volume is firstly fed into a conv layer (kernel-size = 7 × 7 × 7, stride = 2, channel = 64) followed by a max pooling layer (kernel-size = 3 × 3 × 3, stride = 2), 8 residual blocks are then used with each block having 1 conv layer. The channel number is doubled at residual block 3, 5, 7, with stride set as 2 for block 3 and dilation set as 2 for block 5 and set as 4 for block 7. Each conv layer is followed by group-normalization 56 and ReLU activation 52 . In the end, global average pooling is applied to map 512 feature maps to 512 feature values and one linear layer is used for the final prediction, which is a fully-connected layer mapping 512 features to the predicted class score.

Our 3D CNN model based on ResNet10 (residual connection, ReLU activation, group normalization omitted for simplicity). The 3D volume is first fed to a conv layer (kernel-size = 7 × 7 × 7 stride = 2, channel = 64) followed by a max pooling (kernel-size = 3 × 3 × 3, stride = 2). Subsequently, 8 residual blocks are applied with each containing 1 conv layer. Residual blocks parameters: block 1, 2 have kernel-size = 3 × 3 × 3 and channel = 64; block 3, 4 have kernel-size = 3 × 3 × 3 and channel = 128; block 5, 6 have kernel-size = 3 × 3 × 3 and channel = 256; block 7, 8 have kernel-size = 3 × 3 × 3 and channel = 512; stride = 2 is used at conv layer in block 3, while dilation = 2 is used at conv layer in block 5 and dilation = 4 is used at conv layer in block 7. Global average pooling is applied at the end. The classification head is made with one fully-connected layer.

Vision transformer for 3D input pretrained with mask autoencoders

The original 2D ViT 47 is extended to extract features from a 3D volume. Given input 3D diffusion metric \(x \in {R }^{S \times H \times W}\) , the data is reshaped into a sequence of flattened non-overlapping 3D patches \({x}_{p} \in {R }^{N \times (s \cdot h \cdot w)}\) , where ( \(S, H, W\) ) is 3D volume size and ( \(s, h, w\) ) is the 3D patch size, patch number is defined as \(N = SHW/shw\) . As shown in Fig.  3 , for each 3D patch, a linear layer is applied to map voxel values to a latent embedding with dimension \(D\) . A learnable positional embedding with same dimension \(D\) representing each token’s location, is added to the original embedding. The resulting sequence of embeddings for all N patches are fed to the encoder consisting of L alternating layers of multi-head attention and Multi-layer-perceptron (MLP) blocks. A classification token with dimension D is appended to the input embedding sequence, which is designed as a latent representing the entire input sample.The output embedding of the classification token is then fed into a linear prediction head to generate a prediction. In our study, \(S\times H\times W=\) \(182\times 224\times 224\) and \(s\times h\times w=6\times 16\times 16\) , \(D=384\) , \(L=12\) , and the classification head is a fully-connected layer mapping 384 to the predicted class score.

Vision Transformer for Diffusion MRI sex classification: the imaging volume inputted is partitioned into non-overlapping patches. Each patch is projected to patch embedding using a linear patch embedding layer, and added with positional embedding representing the position of the patch. A classification token is appended to the sequence of tokens to learn representation of the entire input sample. The structure of the transformer encoder is shown on the right, which consists of L alternating layers of multi-head attention and multiple-linear-perceptron (MLP) blocks. After the transformer encoder, the corresponding output of the classification token is fed to the classification head to generate prediction results. In our study, a 1 fully-connected layer is used as the classification head. The pretraining of the ViT is shown in the lower part of the figure, with details of the pretraining included in the supplementary information . The ViT encoder from the pretraining is used as the Transformer Encoder as the backbone for the sex classifier shown in the upper part of the figure.

We pretrain the ViT with a 2D + 3D Masked Autoencoders (MAE) modified from 2D MAE 57 , where a specific ratio of patches, defined as \(r\) , is randomly masked and a ViT encoder and auxiliary decoder are trained to predict the values of \(r \times N\) masked patches from \((1 - r)\times N\) unmasked patches. After pretraining, the encoder is finetuned for the target sex classification task with all \(N\) patches fed into it. Since 3D patches are more difficult to predict than 2D patches (especially given the small number of available 3D volumes), we pretrain a 2D ViT encoder with MAE on 2D slices first and use the resulting weights to initialize our 3D ViT model for 3D patches, and further pretrain the model with MAE on 3D volumes. In our study, mask ratio \(r = 0.75\) and the axillary decoder has \(D=192\) , \(L=4\) . Details of the pretraining can be found in the supplementary information .

Model training and evaluation

1031 unique subjects are split into training (831 subjects), validation (100 subjects) and test sets (100 subjects). Training, validation and test sets share the same sex and age distribution, where female and male have a relatively balanced ratio of 27:23. Models’ hyperparameters are tuned based on the performance on the validation set. Models are then trained with the training set and tested on the test set for final prediction results. Classifiers are implemented with pytorch. For fair comparison, all three models use the same training/validation/testing split. Details of the training process are explained in the supplementary information . For ViT, we conducted three experiments: ViT trained from scratch without MAE pretraining, linear probing where the encoder is freezed with weights from MAE pretraining, and only linear prediction head is trained for sex classification, and fine-tuned ViT where the whole model is refined on sex labels. The performance of linear probing can reflect how the feature learnt from pretraining generalizes to the sex classification task, while performance of model trained from scratch can serve as the baseline to examine if the pertaining can bring performance improvement. Details of the training are described in supplementary information .

Occlusion analysis and Wilcoxon signed rank test

We conduct occlusion analysis on the trained models and Wilcoxon signed rank test to identify white matter areas of the brain that contribute significantly to sex classification. We conduct occlusion at the region level and consider the 48 white matter regions defined by the Johns Hopkins University-ICBM-labels-1 mm atlas 43 . Given a trained model for a diffusion metric, we compare the predicted probability for the correct label before and after occlusion of each region in succession, by setting all voxels in the region to the mean white matter value. We apply the Wilcoxon signed rank test with the one-sided alternative hypothesis to the probability changes associated with each region for all subjects in the testing dataset to test whether the decrease in the predicted probability for the correct label is statistically significant. The regions that achieve p-value < 0.05 are considered significant for distinguishing between male and female.

Ethics approval

This study was conducted in compliance with the Health Insurance Portability and Accountability Act and approved by the institutional review board.

Classification results

We use the area under the curve (AUC) of each trained model on the testing dataset to evaluate the model performance. Besides, accuracy, precision and recall are also included. Table 2 shows that our 2D CNN, 3D CNN and ViT (fineturned and linear probing) models all achieved promising AUC for all 3 diffusion metrics with test AUC of > 0.9. For FA and MD, 2D CNN achieved the highest AUC at 0.98 for FA and at 0.97 for MD. 3D CNN and ViT also achieved relatively high AUC (> 0.92). For MK, all models achieved a high AUC above 0.96, and 3D CNN achieved highest performance with AUC of 0.98. The ViT trained from scratch yielded low AUC (< 0.8) for all diffusion metrics. The finetuned ViT and linear probing ViT achieved comparable AUC on all 3 diffusion metrics, indicating that the MAE-pretrained feature extraction layer is directly applicable for the classification task.

Occlusion analysis

2D and 3D CNNs and finetuned ViT are included in the occlusion analysis. The ViT finetuned model is selected for the occlusion analysis despite it has similar performance as the linear probing model, because the finetuned model is refined on the sex classification task. The numbers of regions passing the significance test are summarized in Table 3 . Identified regions are illustrated in Figs. 4 , 5 , and 6 .

WM regions with significant (p < 0.05) impact on classification probability based on occlusion analysis for FA; numbered labels based on JHU-ICBM-1 mm atlas ( https://identifiers.org/neurovault.image:1401 ); 1: middle cerebellar peduncle, 2: pontine crossing tract (a part of middle cerebellar peduncle), 3: genu of corpus callosum, 4: body of corpus callosum, 5: splenium of corpus callosum, 7: corticospinal tract (right), 9: medial lemniscus (right), 10: medial lemniscus (left), 14: superior cerebellar peduncle (left); 17: anterior limb of internal capsule (right), 20: posterior limb of internal capsule (left), 35: cingulum (cingulate gyrus) (right), 37: cingulum (hippocampus) (right), 40: stria terminalis (left), 48: tapetum (left).

WM regions in selected slices with significant (p < 0.05) impact on classification probability based on occlusion analysis for MD; numbered labels based on JHU-ICBM-1 mm atlas ( https://identifiers.org/neurovault.image:1401 ); 1: middle cerebellar peduncle, 2: pontine crossing tract (a part of middle cerebellar peduncle), 3: genu of corpus callosum, 4: body of corpus callosum, 5: splenium of corpus callosum, 5: splenium of corpus callosum, 7: corticospinal tract (right), 13: superior cerebellar peduncle (right), 14: superior cerebellar peduncle (left), 15: cerebral peduncle (right), 17: anterior limb of internal capsule (right), 18: anterior limb of internal capsule (left), 19: posterior limb of internal capsule (right), 20: posterior limb of internal capsule (left), 22: retrolenticular part of internal capsule (left), 25: superior corona radiata (right), 26: superior corona radiata (left), 27: posterior corona radiata (right), 28: posterior corona radiata (left), 31: sagittal stratum (right), 35: Cingulum (cingulate gyrus) (right), 36: cingulum (cingulate gyrus) (left), 37: cingulum (hippocampus) (right), 39: stria terminalis (right), 40: Stria terminalis (left), 42: superior longitudinal fasciculus (left), 43: superior fronto-occipital fasciculus (right).

WM regions in selected slices with significant (p < 0.05) impact on classification probability based on occlusion analysis for MK; numbered labels based on JHU-ICBM-1 mm atlas ( https://identifiers.org/neurovault.image:1401 ); 1: middle cerebellar peduncle, 2: pontine crossing tract (a part of middle cerebellar peduncle), 4: body of corpus callosum, 5: splenium of corpus callosum, 6: fornix (column and body of fornix), 26: superior corona radiata (left), 37: Cingulum (hippocampus) (right), 38: cingulum (hippocampus) (left), 46: uncinate fasciculus (left).

The study provides new evidence of clear sex-related differences in white matter microstructure as captured by diffusion MRI, detected consistently across 3 different end-to-end, deep learning-based image classification models. The reliability of this finding is evident in the fact that high classification performance (test AUC 0.92–0.98) is observed independent of model architecture across 3 major network architecture types and without introducing the biases of complex hand-crafted features and/or manual operations as has been done previously. In addition, white matter regions most influential in model decision are identified and show the location and distribution of greatest microstructural differences.

Use of the three different model architectures intentionally allows us to leverage the different strengths of each of these network families. For example, the 3D CNN incorporates a conventional 3D CNN backbone: while it powerfully captures local features within the volume, a recent study showed that even very deep CNNs with a high number of parameters still have only small effective receptive fields 46 , meaning they rely primarily on their ability to learn local features as opposed to longer distance relationships. On the other hand, ViTs capture global features more readily 47 and the incorporated MAE pretraining task used here also heavily focuses the model on inter-patch correlations than span some distance across the volume. The study finds that both the 3D CNN and ViT models performed very well, suggesting that there are both short-distance and long-distance interactions that show differences in terms of sex-related patterns of white matter microstructure.

The 2D CNN achieved overall best performance for 2 out of 3 diffusion metrics studied. This is felt to be attributable to the fact that the 2D CNN incorporates a design that may allow it to simultaneously capture both local features and global interactions (across all slices), thus making it able to leverage both types of features in the classification task. Specifically, the ResNet18 extracts features from every group of 3 consecutive slices allowing the model to learn from within-slice features and short-range inter-slice features across the 3 slices; by then concatenating features across all 3-slice subvolumes (as opposed to averaging across them as is most commonly done) the model here effectively preserves local features from every 3-slice partition while at the same time, the prediction head is able to learn more global interactions across 3-slice subvolumes. It’s also possible that the simplicity of the 2D CNN model (it had the lowest number of parameters, as shown in Table 4 ) helps to push its generalization capability; although differences between test performance were nominal across all three models, which suggest that they are all comparably generalizable.

The occlusion results show general consistency across models and across diffusion metrics and implicate central white matter tracts and ventral/dorsal hindbrain tracts in contributing to sex-related differences, though results differ slightly across diffusion metrics and models. The number and fractional volume of WM regions significantly contributing to sex classification was highest for 2D CNN (mean number of regions: 15; mean fractional volume: 0.79) compared with 3D CNN (mean number of regions: 2; mean fractional volume: 0.24) and ViT (mean number of regions: 7; mean fractional volume: 0.16), possibly again reflecting differences in the relative facility of these models to tap short-range interactions, long-range interactions, or both. Across the three diffusion metrics, it appears that the 3D CNN classifier focused consistently on large central white matter structures such as the middle cerebellar peduncle and corpus callosum whereas the ViT and 2D CNN models tended to rely on a greater diversity of white matter regions. Another observation is that the corpus callosum was found to be important across all neural network architecture types and diffusion metrics. As sex-related regional brain structure differences have been particularly controversial 17 , 18 , 19 , our work provides new evidence that sex differences do in fact exist in focal regions such as the corpus callosum.

Of note for the ViT, pre-training with MAE was important. ViT is a data-hungry architecture and difficult to train with a limited dataset since it lacks inductive bias such as the locality and translation invariance of CNNs. The MAE pretraining task (to predict masked patches from visible patches) enables the model to learn inter-patch interactions without supervision from data labels. The random masking itself also introduces data diversity to the pretraining, which helps further improve the generalizability of learned features. The benefit of MAE pretraining is clearly demonstrated in the experimental results: without pretraining, ViT trained from scratch yielded much lower performance with test AUC < 0.80. The improvement of each of the other models compared to ViT trained from scratch is statistically significant, with p  < 0.05 achieved with the Wilcoxon signed-rank test that compares the predicted probability of the correct class. With MAE pretraining, the ViT encoder achieved test AUC 0.94–0.96. The end-to-end supervised finetuning brought no additional gain and achieved comparable performance with linear probing, confirming that the size of the training set is insufficient to tune a data-hungry ViT in supervised end-to-end training.

Limitations include the use of only three representative diffusion metrics, though these are chosen based on the fact that they are the most common and easily obtained using a well-established diffusion kurtosis imaging acquisition. Further exploration using modeled diffusion metrics 26 and neurite orientation dispersion and density imaging (NODDI) 60 may yield additional information about sex-related differences in tissue microstructure and help us continue to characterize the underlying biophysical differences between brains of males and females. Additionally, our study is based on DWI with moderate b-value, future study can include datasets with high b-value that are more sensitive to restricted diffusion 61 . Recognizing that the age distribution differs between the female and male cohorts (with the female group having more older people) (Table 1 ), we have separately evaluated the model accuracy on three subgroups broken down by age and found the performance to be comparable across all age groups. For a narrow-band of adults ages 26–30 where aging changes are likely to have little influence, our models continue to achieve high sex classification accuracy. In future work, combined with additional diffusion metrics such as the modeled diffusion metrics 26 and NODDI 60 , the study can be extended to examine the sex differences in age ranges other than young adults, which can shed light on how sex differences progress in life span. Finally, occlusion analysis used the standard JHU-ICBM-1 mm atlas for white matter parcellation which has sizable variation in size of regions which could potentially bias regional importance; any affect region size has on region importance is limited, however, as the results for relative importance of regions does not correlate well with region size. Besides, our 2D CNN is based on sagittal slices, in future work, the new 2D CNN can be designed to leverage information from all three views. This study does not use the 3 diffusion metrics as combined input to neural networks due to GPU memory constraint; neural network architectures that can efficiently take multiple 3D volumes as combined input can be explored in the future work.

Overall, our results provide new evidence and insights to support that sex differences exist in human brain microstructure both in local features (e.g., within central white matter structures such as the middle cerebellar peduncle and corpus callosum) and in global features (like long-distance interactions). Capturing complex microstructural differences is challenging using conventional statistical methods or single-approach machine learning network inputting handcrafted features. Our work demonstrates a unique approach: that by leveraging multiple neural networks with completely different architecture design, it allows us to capture complementary information and makes our results independent of model architecture choices. When it comes to using advanced machine learning architectures that are data-hungry, self-supervised learning can be used to pretrain models and enable such neural networks to be leveraged for medical imaging studies that lack tremendous datasets such as those available for non-medical, computer vision work. Such a framework can be further adapted to study other neurological disorders.

This study provides new evidence of clear sex-related differences in brain white matter microstructure of healthy young adults detected using in vivo diffusion MRI without hand-crafting or manually manipulating the imaging data. We show this across 3 different end-to-end deep neural networks and 3 commonly used diffusion MRI metrics. Even after registering diffusion MR volumes to a template so as to remove macroscopic anatomical differences such as overall brain size and contour, results show that sex differences exist in diffusion anisotropy (FA), mean diffusivity (MD) and tissue complexity (MK) of brain white matter. Our experiments further suggest there are both local as well as longer-distance microstructural organizational features that differ between sexes. In particular, the central white matter appears specifically implicated. In addition, this study provides a framework to study microstructural differences in the human brain using multiple deep neural network architectures, which help capture complex microscopic features challenging for statistical methods. Further study is needed to determine whether and how these microstructural differences influence brain health and disease in both men and women.

Data availability

The dataset used in this study can be downloaded from the Human Connectome Project via Amazon S3 at https://db.humanconnectome.org/ .

Dorfberger, S., Adi-Japha, E. & Karni, A. Sex differences in motor performance and motor learning in children and adolescents: an increasing male advantage in motor learning and consolidation phase gains. Behav. Brain Res. 198 (1), 165–171 (2009).

Article   PubMed   Google Scholar  

Moreno-Briseño, P. et al. Sex-related differences in motor learning and performance. Behav. Brain Funct. 6 (1), 1–4 (2010).

Article   Google Scholar  

Satterthwaite, T. D. et al. Linked sex differences in cognition and functional connectivity in youth. Cereb. Cortex. 25 (9), 2383–2394 (2015).

Voyer, D., Voyer, S. D. & Saint-Aubin, J. Sex differences in visual-spatial working memory: A meta-analysis. Psychonomic Bull. Rev. 24 , 307–334 (2017).

Duff, S. J. & Hampson, E. A sex difference on a novel spatial working memory task in humans. Brain Cogn. 47 (3), 470–493 (2001).

Article   CAS   PubMed   Google Scholar  

Kaufman, S. B. Sex differences in mental rotation and spatial visualization ability: Can they be accounted for by differences in working memory capacity?. Intelligence 35 (3), 211–223 (2007).

Asperholm, M., Van Leuven, L. & Herlitz, A. Sex differences in episodic memory variance. Front. Psychol. 11 , 613 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Asperholm, M. et al. What did you do yesterday? A meta-analysis of sex differences in episodic memory. Psychol. Bull. 145 (8), 785 (2019).

Herlitz, A., Airaksinen, E. & Nordström, E. Sex differences in episodic memory: The impact of verbal and visuospatial ability. Neuropsychology 13 (4), 590 (1999).

Werling, D. M. & Geschwind, D. H. Sex differences in autism spectrum disorders. Curr. Opin. Neurol. 26 (2), 146 (2013).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Baizabal-Carvallo, José Fidel, and Joseph Jankovic. "Sex differences in patients with Tourette syndrome." CNS spectrums (2022): 1–7.

Picco, L. et al. Gender differences in major depressive disorder: Findings from the Singapore Mental Health Study. Singapore Med. J. 58 (11), 649 (2017).

Pigott, T. A. Anxiety disorders in women. Psychiatric Clin. 26 (3), 621–672 (2003).

Google Scholar  

Ruigrok, A. N. V. et al. A meta-analysis of sex differences in human brain structure. Neurosci. Biobehav. Rev. 39 , 34–50 (2014).

Lotze, M. et al. Novel findings from 2838 adult brains on sex differences in gray matter brain volume. Sci. Rep. 9 (1), 1671 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Ritchie, S. J. et al. Sex differences in the adult human brain: Evidence from 5216 UK biobank participants. Cerebral Cortex 28 (8), 2959–2975 (2018).

Adeli, E. et al. Deep learning identifies morphological determinants of sex differences in the pre-adolescent brain. NeuroImage 223 , 117293 (2020).

Jahanshad, N. & Thompson, P. M. Multimodal neuroimaging of male and female brain structure in health and disease across the life span. J. Neurosci. Res. 95 (1–2), 371–379 (2017).

Luders, E., Toga, A. W. & Thompson, P. M. Why size matters: Differences in brain volume account for apparent sex differences in callosal anatomy: The sexual dimorphism of the corpus callosum. Neuroimage 84 , 820–824 (2014).

Jiang, X. & Nardelli, J. Cellular and molecular introduction to brain development. Neurobiol. Disease 92 , 3–17 (2016).

Article   CAS   Google Scholar  

Finch, C. E. Neurons, glia, and plasticity in normal brain aging. Neurobiol. Aging 24 , S123–S127 (2003).

Von Bernhardi, R., Eugenín-von Bernhardi, L. & Eugenín, J. Microglial cell dysregulation in brain aging and neurodegeneration. Front. Aging Neurosci. 7 , 124 (2015).

Svolos, P. et al. The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives. Cancer Imaging 14 , 1–20 (2014).

Han, J. et al. Uncovering sex differences of rodent microglia. J. Neuroinflam. 18 (1), 1–11 (2021).

Article   MathSciNet   Google Scholar  

Guneykaya, D. et al. Transcriptional and translational differences of microglia from male and female brains. Cell Rep. 24 (10), 2773–2783 (2018).

Novikov, D. S. et al. Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation. NMR Biomed. 32 (4), e3998 (2019).

Zhang, Y. et al. White matter damage in frontotemporal dementia and Alzheimer’s disease measured by diffusion MRI. Brain 132 (9), 2579–2592 (2009).

Harrison, J. R. et al. Imaging Alzheimer’s genetic risk using diffusion MRI: A systematic review. NeuroImage: Clin. 27 , 102359 (2020).

Bergamino, M. et al. Assessing white matter pathology in early-stage Parkinson disease using diffusion MRI: A systematic review. Front. Neurol. 11 , 314 (2020).

De Santis, S. et al. Evidence of early microstructural white matter abnormalities in multiple sclerosis from multi-shell diffusion MRI. NeuroImage: Clin. 22 , 101699 (2019).

Kanaan, R. A. et al. Gender differences in white matter microstructure. PloS ONE 7 (6), e38272 (2012).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Seunarine, K. K. et al. Sexual dimorphism in white matter developmental trajectories using tract-based spatial statistics. Brain Connect. 6 (1), 37–47 (2016).

Bryant, K. L., Grossi, G., and Kaiser, A. Feminist interventions on the sex/gender question in neuroimaging research (2019).

Yeung, Hon Wah, et al. "Pipeline comparisons of convolutional neural networks for structural connectomes: predicting sex across 3152 participants." 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) . IEEE, 2020.

He, Hao, et al. "Model and predict age and sex in healthy subjects using brain white matter features: a deep learning approach." 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2022.

Van Essen, D. C. et al. The WU-Minn human connectome project: An overview. Neuroimage 80 , 62–79 (2013).

Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80 , 105–124 (2013).

Ades-Aron, B. et al. Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline. Neuroimage 183 , 532–543 (2018).

Vos, S. B. et al. The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain. Neuroimage 59 (3), 2208–2216 (2012).

Szczepankiewicz, F. et al. Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors. Neuroimage 104 , 241–252 (2015).

Nonomura, Y. et al. Relationship between bone marrow cellularity and apparent diffusion coefficient. J. Magn. Resonance Imaging 13 (5), 757–760 (2001).

Chung, S. et al. Investigating brain white matter in foot- ball players with and without concussion using a biophysical model from multishell diffusion mri. Am. J. Neuroradiol. 43 (6), 823–828. https://doi.org/10.3174/ajnr.a7522 (2022).

Mori, S. et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40 (2), 570–582 (2008).

Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 , S208–S219 (2004).

Liu, Y. et al. Efficient training of visual transformers with small datasets. Adv. Neural Inform. Process. Syst. 34 , 23818–23830 (2021).

Ding, X., et al. Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.

Dosovitskiy, A., et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929 (2020).

Ingalhalikar, M. et al. Sex differences in the structural connectome of the human brain. Proc. Natl. Acad. Sci. 111 (2), 823–828 (2014).

Article   ADS   CAS   PubMed   Google Scholar  

Ryman, S. G. et al. Sex differences in the relationship between white matter connectivity and creativity. NeuroImage 101 , 380–389 (2014).

He, K., et al. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition . 2016.

Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." International conference on machine learning . pmlr, 2015.

Nair, Vinod, and Geoffrey E. Hinton. "Rectified linear units improve restricted boltzmann machines." Proceedings of the 27th international conference on machine learning (ICML-10) . 2010.

Hara, Kensho, Hirokatsu Kataoka, and Yutaka Satoh. "Learning spatio-temporal features with 3d residual networks for action recognition." Proceedings of the IEEE international conference on computer vision workshops . 2017.

Carreira, Joao, and Andrew Zisserman. "Quo vadis, action recognition? a new model and the kinetics dataset." proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2017.

Chen, Sihong, Kai Ma, and Yefeng Zheng. "Med3d: Transfer learning for 3d medical image analysis." arXiv:1904.00625 (2019).

Wu, Yuxin, and Kaiming He. "Group normalization." Proceedings of the European conference on computer vision (ECCV) . 2018.

He, K., et al. Masked autoencoders are scalable vision learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.

Liu, Z., et al. Video swin transformer. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.

Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12 , 2825–2830 (2011).

MathSciNet   Google Scholar  

Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 61 (4), 1000–1016 (2012).

Han, C. et al. A comparison of high b-value vs standard b-value diffusion-weighted magnetic resonance imaging at 3.0 T for medulloblastomas. Br. J. Radiol. 88 (1054), 20150220 (2015).

Cox, S. R. et al. Ageing and brain white matter structure in 3513 UK Biobank participants. Nat. Commun. 7 (1), 13629 (2016).

Lawrence, K. E. et al. Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: A UK biobank study. Brain Imaging Behav. 15 (6), 2813–2823 (2021).

Download references

Acknowledgements

This work was supported by the National Institutes of Health, National Institute of Neurological Disorders and Stroke [grant numbers: R01NS119767, R01NS131458, R01 NS119767-01A1, R01 NS039135-11, R56 NS119767]; National Institute of Biomedical Imaging and Bioengineering [grant number: P41 EB017183] and Department of Defense [grant number: W81XWH2010699].

Author information

These authors contributed equally: Junbo Chen, and Vara Lakshmi Bayanagari.

Authors and Affiliations

Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, 370 Jay Street, 9th Floor, Brooklyn, NY, 11201, USA

Junbo Chen, Vara Lakshmi Bayanagari & Yao Wang

Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA

Sohae Chung & Yvonne W. Lui

Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA

Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA

You can also search for this author in PubMed   Google Scholar

Contributions

J.C.: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Software; Validation; Visualization; Roles/Writing—original draft; Writing—review & editing; V.L.B.: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Software; Validation; Visualization; Roles/Writing—original draft; Writing—review & editing; S.C.: Data curation; Roles/Writing—original draft; Writing—review & editing; Y.W.: Supervision; Conceptualization; Methodology; Resources; Roles/Writing—original draft; Writing—review & editing; Project administration; Funding acquisition; Y.W.L.: Supervision; Conceptualization; Resources; Roles/Writing—original draft; Writing—review & editing; Project administration; Funding acquisition.

Corresponding author

Correspondence to Junbo Chen .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Chen, J., Bayanagari, V.L., Chung, S. et al. Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure. Sci Rep 14 , 9835 (2024). https://doi.org/10.1038/s41598-024-60340-y

Download citation

Received : 25 November 2023

Accepted : 22 April 2024

Published : 14 May 2024

DOI : https://doi.org/10.1038/s41598-024-60340-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.