Deep learning in bioinformatics

Clicks: 3
ID: 292206
2016
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Ranked #52 of 55 articles by views in Briefings in bioinformatics

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Abstract
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-the-art 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.
Reference Key
openalex_W2311607323 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Seonwoo Min, Byunghan Lee, Sungroh Yoon
Journal Briefings in bioinformatics
Year 2016
DOI
10.1093/bib/bbw068
URL
https://doi.org/10.1093/bib/bbw068
Keywords Keywords not found

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