Journal
BRIEFINGS IN BIOINFORMATICS
Volume 18, Issue 5, Pages 851-869Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbw068
Keywords
deep learning; neural network; machine learning; bioinformatics; omics; biomedical imaging; biomedical signal processing
Funding
- National Research Foundation (NRF) of Korea grants - Korean Government (Ministry of Science, ICT and Future Planning) [2014M3C9A3063541, 2015M3A9A7029735]
- Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) - Ministry of Health Welfare [HI15C3224]
- SNU ECE Brain Korea 21+ project
- National Research Foundation of Korea [2015M3A9A7029725, 2016H1A2A1906538, 2015M3A9A7029735] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-theart performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.
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