Sunday, October 2, 4-6 p.m.

Abstract

In this era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important problems in bioinformatics. Meanwhile, deep learning has advanced rapidly since the early 2000s, and now demonstrates state-of-the-art performance in various fields. Accordingly, the application of deep learning in bioinformatics to gain insight from data is emphasized both in academia and industry. This tutorial will review deep learning in the bioinformatics and presents examples of current research. To provide a useful and comprehensive perspective, the presenter will categorize related research both by 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, there will be discussion on theoretical and practical issues of deep learning in bioinformatics and suggestions for future research directions. This tutorial will provide valuable insight and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.

[Tutorial Slides]


Presenter

Prof. Sungroh Yoon, Seoul National University

Dr. Yoon received the B.S. degree from Seoul National University, South Korea, and the M.S. and Ph.D. degrees from Stanford University, CA, respectively, all in electrical engineering. He held research positions with Stanford University, CA, Intel Corporation, Santa Clara, CA, and Synopsys, Inc., Mountain View, CA. He was an Assistant Professor with the School of Electrical Engineering, Korea University, from 2007 to 2012. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, Seoul National University, South Korea. Prof. Yoon is the recipient of 2013 IEEE/IEIE Joint Award for Young IT Engineers. His research interests include deep learning, machine learning, data-driven artificial intelligence, and large-scale applications including biomedicine.


References

  • Seonwoo Min, Byunghan Lee, and Sungroh Yoon, "Deep learning in bioinformatics," Briefings in Bioinformatics, in press.
  • Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey, "Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning," Nature Biotechnology, vol. 33, no. 8, pp. 825-6, 2015.
  • David R Kelley, Jasper Snoek, and John L Rinn, "Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks," Genome Research, in press.
  • Jian Zhou, and Olga G Troyanskaya, "Predicting effects of noncoding variants with deep learning-based sequence model," Nature Methods, vol. 12, no. 10, pp. 931-4, 2015.
  • Sai Zhang, Jingtian Zhou, Hailin Hu, Haipeng Gong, Ligong Chen, Chao Cheng, and Jianyang Zeng, "A deep learning framework for modeling structural features of RNA-binding protein targets," Nucleic Acids Research, vol. 44, no. 4, pp. e32, 2016.
  • Byunghan Lee, Junghwan Baek, Seunghyun Park, and Sungroh Yoon, "deepTarget: End-to-end learning framework for microRNA target prediction using deep recurrent neural networks," Proceedings of the 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), 2016.
  • Jun Xu, Lei Xiang, Qinshan Liu, Hannah Gilmore, Jianzhong Wu, Jinghai Tang, and Anant Madabhushi, "Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images," IEEE Transactions on Medical Imaging, vol. 35, no. 1, pp. 119-30, 2016.
  • Sungmin Lee, Minsuk Choi, Hyun-soo Choi, Moon Seok Park, and Sungroh Yoon, "FingerNet: Deep learning-based robust finger joint detection from radiographs," IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 619-22, 2015.
  • Holger R Roth, Le Lu, Amal Farag, Hoo-Chang Shin, Jiamin Liu, Evrim B Turkbey, and Ronald M Summers, "Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation," Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 556-64, 2015.
  • Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, and Hugo Larochelle, "Brain tumor segmentation with deep neural networks," Medical Image Analysis, in press.
  • Piotr Mirowski, Deepak Madhavan, Yann LeCun, and Ruben Kuzniecky, "Classification of patterns of EEG synchronization for seizure prediction," Clinical Neurophysiology, vol. 120, no. 11, pp. 1927-40, 2009.