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Modern deep learning in bioinformatics - 2020

Modern Deep Learning In Bioinformatics

Research Area:  Machine Learning


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.


Author(s) Name:  Haoyang Li, Shuye Tian, Yu Li, Qiming Fang, Renbo Tan, Yijie Pan, Chao Huang, Ying Xu, Xin Gao

Journal name:  Journal of Molecular Cell Biology

Conferrence name:  

Publisher name:   Oxford University Press

DOI:  10.1093/jmcb/mjaa030

Volume Information:  Volume 12, Issue 11, November 2020, Pages 823–827,