Transcription of Deep Learning in Bioinformatics
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Deep Learning in Bioinformatics
Deep Learning in Bioinformatics Seonwoo Min1, Byunghan Lee1, and Sungroh Yoon1,2*. 1. Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea 2. Interdisciplinary Program in Bioinformatics , Seoul National University, Seoul 08826, Korea 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 ( , omics, biomedical imaging, biomedical signal processing) and deep Learning architecture ( , deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study.
Deep Learning in Bioinformatics . Seonwoo Min. 1, Byunghan Lee. 1, and Sungroh Yoon. 1,2 * 1Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea 2Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea . Abstract. In the era of big data, transformation of biomedical big data into valuable …
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