Modern Deep Learning in Bioinformatics.

Haoyang Li, Shuye Tian, Yu Li, Qiming Fang, Renbo Tan, Yijie Pan, Chao Huang, Ying Xu, Xin Gao

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


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.
Original languageEnglish (US)
JournalJournal of Molecular Cell Biology
StatePublished - Jun 24 2020


Dive into the research topics of 'Modern Deep Learning in Bioinformatics.'. Together they form a unique fingerprint.

Cite this