multiple assignment cluster

Multi-assignment clustering: Machine learning from a biological perspective

Affiliations.

• 1 School of Bioscience, University of Skövde, Skövde, Sweden. Electronic address: [email protected].
• 2 School of Informatics, University of Skövde, Skövde, Sweden.
• 3 School of Informatics, University of Skövde, Skövde, Sweden; Department of Computer Science and Informatics, School of Engineering, Jönköping University, Jönköping, Sweden.
• 4 Takara Bio Europe AB, Gothenburg, Sweden.
• 5 School of Bioscience, University of Skövde, Skövde, Sweden.
• PMID: 33285150
• DOI: 10.1016/j.jbiotec.2020.12.002

A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc).

Keywords: Annotation enrichment; Clustering; K-means; Multiple cluster assignment; Pathways; Transcriptomics.

Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

• Algorithms*
• Cluster Analysis
• Gene Expression Profiling
• Machine Learning*

Sequential, Multiple Assignment, Randomized Trials (SMART)

• Living reference work entry
• First Online: 12 August 2021
• Cite this living reference work entry

• Nicholas J. Seewald 3 ,
• Olivia Hackworth 3 &
• Daniel Almirall 3  

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A dynamic treatment regimen (DTR) is a prespecified set of decision rules that can be used to guide important clinical decisions about treatment planning. This includes decisions concerning how to begin treatment based on a patient’s characteristics at entry, as well as how to tailor treatment over time based on the patient’s changing needs. Sequential, multiple assignment, randomized trials (SMARTs) are a type of experimental design that can be used to build effective dynamic treatment regimens (DTRs). This chapter provides an introduction to DTRs, common types of scientific questions researchers may have concerning the development of a highly effective DTR, and how SMARTs can be used to address such questions. To illustrate ideas, we discuss the design of a SMART used to answer critical questions in the development of a DTR for individuals diagnosed with alcohol use disorder.

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Adaptive Intervention Designs in Substance Use Prevention

Randomized Controlled Trials in Substance Abuse Treatment Research: Fundamental Aspects and New Developments in Random Assignment Strategies, Comparison/Control Conditions, and Design Characteristics

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Acknowledgments

Funding was provided by the National Institutes of Health (P50DA039838, R01DA039901) and the Institute for Education Sciences (R324B180003). Funding for the ExTENd study, which was used to illustrate ideas, was provided by the National Institutes of Health (R01AA014851; PI: David Oslin).

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Nicholas J. Seewald, Olivia Hackworth & Daniel Almirall

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Samuel Oschin Comprehensive Cancer Insti, WEST HOLLYWOOD, CA, USA

Steven Piantadosi

Bloomberg School of Public Health, Johns Hopkins Center for Clinical Trials Bloomberg School of Public Health, Baltimore, MD, USA

Curtis L. Meinert

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Statistician, MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, London, UK

Babak Choodari-Oskooei

MRC Clinical Trials Unit and Institute of Clinical Trials and Methodology, University College of London, London, England

Mahesh Parmar

The Johns Hopkins Center for Clinical Trials and Evidence Synthesis, Johns Hopkins School of Public Health, Baltimore, MA, USA

Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

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Seewald, N.J., Hackworth, O., Almirall, D. (2021). Sequential, Multiple Assignment, Randomized Trials (SMART). In: Piantadosi, S., Meinert, C.L. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52677-5_280-1

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DOI : https://doi.org/10.1007/978-3-319-52677-5_280-1

Received : 09 April 2021

Accepted : 28 April 2021

Published : 12 August 2021

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-52677-5

Online ISBN : 978-3-319-52677-5

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