Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

近年来，生物学研究的飞速发展极大地丰富了生物学和医学数据资源。基于深度学习的技术已被成功地用于处理该领域的数据，并且即使在高维，非结构和黑盒生物学数据上，它们也表现出了最先进的性能。本研究的目的是概述生物学和医学中使用的基于深度学习的技术及其最新应用。特别是，我们介绍了深度学习的基础知识，然后回顾了将此类方法应用于生物信息学，生物医学成像，生物医学和药物发现的成功。我们还将讨论该领域的挑战和局限性，并概述进一步研究的可能方向。