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.

分子技术的进展推动了与生物学和生物医学相关的数据集的巨大增长。这些进展与机器学习中的深度学习子领域相似。可微分编程工具箱中的组件使深度学习成为可能，使计算机科学家能够用灵活和有效的工具解决越来越多的问题。然而，这些工具中许多尚未充分应用于计算生物学和生物信息学领域。在这个观点中，我们对其中一些进展进行了调查，并强调它们在生命科学中的应用，以增加从业人员对将专业知识与新兴的深度学习架构工具相结合的新机遇的认识。