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Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00214

Recent Advances of Deep Learning in Bioinformatics and Computational Biology

  • 1Epigenetics & Function Group, Hohai University, China
  • 2School of Medicine, Shanghai Jiao Tong University, China

Extracting inherent valuable knowledge from omics big data remains as a haunting problem in bioinformatics and computational biology. Deep learning, as an emerging branch from machine learning, has exhibited unprecedented performance in quite a few applications from academia and industry. We highlight the difference and similarity in widely utilized models in deep learning studies, through discussing their basic structures, and reviewing diverse applications and disadvantages. We anticipate the work can serve as a meaningful perspective for further development of its theory, algorithm and application in bioinformatic and computational biology.

Keywords: Computational Biology, algorithm, bioinformatics, deep learning, application

Received: 20 Aug 2018; Accepted: 27 Feb 2019.

Edited by:

Juan Caballero, Universidad Autónoma de Querétaro, Mexico

Reviewed by:

Wenhai Zhang, Hengyang Normal University, China
Zhuliang Yu, South China University of Technology, China  

Copyright: © 2019 Tang, Pan, Kang and Khateeb. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Binhua Tang, Hohai University, Epigenetics & Function Group, Nanjing, China, bh.tang@outlook.com