A Guide to Machine Learning for Biologists

Overview

Journal Nat Rev Mol Cell Biol

Specialties Cell Biology
Molecular Biology

Date 2021 Sep 14

PMID 34518686

Citations 464

Authors

Joe G Greener

Shaun M Kandathil

Lewis Moffat

David T Jones

Affiliations

Soon will be listed here.

Abstract

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.

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