Computational biology: deep learning

[alxndrkalinin]

https://doi.org/10.1042/ETLS20160025

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.

Rather brief review of some recent applications

[agitter]

Their model diagrams are nice. That is the sort of thing that our reviewers suggested we add to provide more intuition about the network architectures.

We cite several other reviews in our introduction and could add this one as well.