  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.
