|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Applications of Deep Learning and Reinforcement Learning to Biological Data|
Kaiser, Mohammed Shamim
convolutional neural network (CNN)
deep autoencoder (DA)
deep belief network (DBN)
deep learning performance
recurrent neural network (RNN)
|Citation:||Mahmud M, Kaiser MS, Hussain A & 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. https://doi.org/10.1109/TNNLS.2018.2790388.|
|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.|
|Rights:||X © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information|
|1711.03985.pdf||Fulltext - Accepted Version||1.78 MB||Adobe PDF||View/Open|
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