title: Deep learning in bioinformatics.
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.
authors.forename: Seonwoo
authors.initials: S
authors.lastname: Min
authors.forename: Byunghan
authors.initials: B
authors.lastname: Lee
authors.forename: Sungroh
authors.initials: S
authors.lastname: Yoon
mesh_terms: D019295:Computational Biology; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer
keywords: bioinformatics; biomedical imaging; biomedical signal processing; deep learning; machine learning; neural network; omics