Opportunities in deep learning methods development for computational biology

TL;DR

This paper surveys recent deep learning advancements and their potential applications in computational biology, aiming to bridge the gap between machine learning tools and biosciences to foster innovative research.

Contribution

It highlights emerging deep learning tools and showcases their potential in computational biology, encouraging integration of expert knowledge with new architectures.

Findings

Deep learning tools are underutilized in biology and bioinformatics.

Examples demonstrate successful application of deep learning in biosciences.

Awareness of deep learning opportunities can accelerate biological research.

Abstract

Advances in molecular technologies underlie an enormous growth in the size of data sets pertaining to biology and biomedicine. These advances parallel those in the deep learning subfield of machine learning. Components in the differentiable programming toolbox that makes deep learning possible are allowing computer scientists to address an increasingly large array of problems with flexible and effective tools. However many of these tools have not fully proliferated into the computational biology and bioinformatics fields. In this perspective we survey some of these advances and highlight exemplary examples of their utilization in the biosciences, with the goal of increasing awareness among practitioners of emerging opportunities to blend expert knowledge with newly emerging deep learning architectural tools.