  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.
