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. 2017 Nov 14;1(3):257-274.
doi: 10.1042/ETLS20160025.

Computational biology: deep learning

William Jones  1 , Kaur Alasoo  1 , Dmytro Fishman  2   3 , Leopold Parts  1   2
Affiliations

Computational biology: deep learning

William Jones et al. Emerg Top Life Sci. .

Abstract

Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.

Keywords: bioinformatics; computational biology; deep learning.

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Conflict of interest statement

The Authors declare that there are no competing interests associated with the manuscript.

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