Reference : Ten Quick Tips for Biomarker Discovery and Validation Analyses Using Machine Learning

	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Biotechnology Life sciences : Multidisciplinary, general & others Human health sciences : Multidisciplinary, general & others
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/51787

Title :	Ten Quick Tips for Biomarker Discovery and Validation Analyses Using Machine Learning
Language :	English
Author, co-author :	Diaz-Uriarte, R. [> >]
	Gómez de Lope, Elisa [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science]
	Giugno, R. [> >]
	Fröhlich, H. [> >]
	Nazarov, Petr [University of Luxembourg > >]
	Nepomuceno-Chamorro, I. A. [> >]
	Rauschenberger, Armin [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science]
	Glaab, Enrico [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science]
Publication date :	2022
Journal title :	PLoS Computational Biology
Publisher :	Public Library of Science
Volume :	18
Issue/season :	8
Pages :	e1010357
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	1553-734X
e-ISSN :	1553-7358
City :	San Francisco
Country :	CA
Keywords :	[en] biomarkers ; machine learning ; stratification ; prediction ; diagnosis ; prognosis ; data science ; decision medicine ; decision support
Abstract :	[en] High-throughput experimental methods for biosample profiling and growing collections of clinical and health record data provide ample opportunities for biomarker discovery and medical decision support. However, many of the new data types, including single-cell omics and high-resolution cellular imaging data, also pose particular challenges for data analysis. A high dimensionality of the data in relation to small numbers of available samples, influences of additive and multiplicative noise, large numbers of uninformative or redundant data features, outliers, confounding factors and imbalanced sample group numbers are all common characteristics of current biomedical data collections. While first successes have been achieved in developing clinical decision support tools using multifactorial omics data, there is still an unmet need and great potential for earlier, more accurate and robust diagnostic and prognostic tools for many complex diseases. Here, we provide a set of broadly applicable tips to address some of the most common pitfalls and limitations for biomarker signature development, including supervised and unsupervised machine learning, feature selection and hypothesis testing approaches. In contrast to previous guidelines discussing detailed aspects of quality control, statistics or study reporting, we give a broader overview of the typical challenges and sort the quick tips to address them chronologically by the study phase (starting with study design, then covering consecutive phases of biomarker signature discovery and validation, see also the overview in Fig. 1). While these tips are not comprehensive, they are chosen to cover what we consider as the most frequent, significant, and practically relevant issues and risks in biomarker development. By pointing the reader to further relevant literature on the covered aspects of biomarker discovery and validation, we hope to provide an initial guideline and entry point into the more detailed technical and application-specific aspects of this field.
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Funders :	Fonds National de la Recherche - FnR ; European Union - Horizon 2020
Name of the research project :	FNR14599012 > Enrico Glaab > DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’S Disease > 01/05/2021 > 30/04/2024 > 2020
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/51787
DOI :	10.1371/journal.pcbi.1010357
Other URL :	https://doi.org/10.1371/journal.pcbi.1010357
Mentions required by the publisher for OA :	The original publication is available at: https://doi.org/10.1371/journal.pcbi.1010357
Framework Programme / European Project :	H2020 ; 874825 - Personalized Medicine Trials (PERMIT)
FnR project :	FnR ; FNR14599012 > Enrico Glaab > DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’S Disease > 01/05/2021 > 30/04/2024 > 2020

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