Applications of deep learning and reinforcement learning to biological data

Mahmud, M. ORCID: 0000-0002-2037-8348, Kaiser, M.S., Hussain, A. and Vassanelli, S., 2018. Applications of deep learning and reinforcement learning to biological data. IEEE Transactions on Neural Networks and Learning Systems, 29 (6), pp. 2063-2079. ISSN 2162-237X

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Abstract

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Item Type: Journal article
Publication Title: IEEE Transactions on Neural Networks and Learning Systems
Creators: Mahmud, M., Kaiser, M.S., Hussain, A. and Vassanelli, S.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: June 2018
Volume: 29
Number: 6
ISSN: 2162-237X
Identifiers:
NumberType
10.1109/tnnls.2018.2790388DOI
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 23 Jul 2018 12:55
Last Modified: 23 Jul 2018 12:55
URI: http://irep.ntu.ac.uk/id/eprint/34131

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