Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.

中文翻译：

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生物信息学中的现代深度学习

深度学习 (DL) 在生物信息学领域的应用呈现出爆炸性增长，并在挖掘大规模生物和生物医学数据中隐藏的复杂关系方面展现出令人兴奋的潜力。已经发表了许多关于此类应用程序的综合评论，从具有未来前景的高级评论到主要作为教程的评论。这些评论为 DL 在生物信息学中的应用提供了出色的介绍和指南，涵盖多种类型的机器学习 (ML) 问题、不同的 DL 架构以及一系列生物/生物医学问题。然而，这些评论大多数都集中在以前的研究上，而原则性深度学习领域的当前趋势以及对其未来发展和生物学和生物医学潜在新应用的看法仍然很少。我们将重点关注现代深度学习、深度学习原则领域的当前趋势和未来方向，并提出生物信息学中新的主要应用。