Greener, JG;
Kandathil, SM;
Moffat, L;
Jones, DT;
(2022)
A guide to machine learning for biologists.
Nature Reviews Molecular Cell Biology
, 23
pp. 40-55.
10.1038/s41580-021-00407-0.
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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.
| Type: | Article |
|---|---|
| Title: | A guide to machine learning for biologists |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41580-021-00407-0 |
| Publisher version: | https://doi.org/10.1038/s41580-021-00407-0 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Bioinformatics, Computational biology and bioinformatics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10134478 |
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