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Modern deep learning in bioinformatics
Haoyang Li1,2,† , Shuye Tian3,† , Yu Li4,† , Qiming Fang5 , Renbo Tan1 , Yijie Pan6 , Chao Huang6 , Ying Xu1,2,7,* , Xin Gao4,*
1Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun 130033, China
2MOE Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China
3Department of Biology, Southern University of Science and Technology, Shenzhen 518055, China
4Computational Bioscience Research Center (CBRC), Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
5School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
6Ningbo Institute of Information Technology Application, Chinese Academy of Sciences, Ningbo 315040, China
7Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
These authors contributed equally to this work
*Correspondence to:Ying Xu , Email:xyn@uga.edu Xin Gao , Email:xin.gao@kaust.edu.sa
J Mol Cell Biol, Volume 12, Issue 11, November 2020, Pages 823-827  https://doi.org/10.1093/jmcb/mjaa030

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.