计算生物学中深度学习方法开发的机遇
Opportunities in deep learning methods development for computational biology
AI总结：
本文综述可微编程与深度学习架构的最新进展及其在生物科学中的典型应用，指出其尚未充分普及，并倡导融合专家知识以把握计算生物学方法开发的新机遇。
摘要：
分子技术的进步推动了生物学和生物医学相关数据集规模的巨大增长。这些进展与机器学习中深度学习子领域的发展同步。使深度学习成为可能的可微编程工具箱中的组件，正让计算机科学家能够用灵活且有效的工具解决越来越广泛的问题。然而，其中许多工具尚未在计算生物学和生物信息学领域得到充分普及。在这篇观点文章中，我们综述了其中一些进展，并重点介绍它们在生物科学中应用的典型示例，旨在提高从业者对将专家知识与新兴深度学习架构工具相融合这一新兴机遇的认识。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
