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BioData Min. 2017 Dec 8;10:35. doi: 10.1186/s13040-017-0155-3. eCollection 2017.

Ten quick tips for machine learning in computational biology.

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1
Princess Margaret Cancer Centre, PMCR Tower 11-401, 101 College Street, Toronto, Ontario, M5G 1L7 Canada.

Abstract

Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.

KEYWORDS:

Bioinformatics; Biomedical informatics; Computational biology; Computational intelligence; Data mining; Health informatics; Machine learning; Tips

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