Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26814
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dc.contributor.authorMahmud, Muftien_UK
dc.contributor.authorKaiser, Mohammed Shamimen_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.authorVassanelli, Stefanoen_UK
dc.date.accessioned2018-03-03T04:38:13Z-
dc.date.available2018-03-03T04:38:13Z-
dc.date.issued2018-06en_UK
dc.identifier.urihttp://hdl.handle.net/1893/26814-
dc.description.abstractRapid 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.en_UK
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.relationMahmud 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.2018.2790388en_UK
dc.rightsX © 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 informationen_UK
dc.subjectBioimagingen_UK
dc.subjectbrain-machine interfacesen_UK
dc.subjectconvolutional neural network (CNN)en_UK
dc.subjectdeep autoencoder (DA)en_UK
dc.subjectdeep belief network (DBN)en_UK
dc.subjectdeep learning performanceen_UK
dc.subjectmedical imagingen_UK
dc.subjectomicsen_UK
dc.subjectrecurrent neural network (RNN)en_UK
dc.titleApplications of Deep Learning and Reinforcement Learning to Biological Dataen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1109/TNNLS.2018.2790388en_UK
dc.identifier.pmid29771663en_UK
dc.citation.jtitleIEEE Transactions on Neural Networks and Learning Systemsen_UK
dc.citation.issn2162-2388en_UK
dc.citation.issn2162-237Xen_UK
dc.citation.volume29en_UK
dc.citation.issue6en_UK
dc.citation.spage2063en_UK
dc.citation.epage2079en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.author.emailahu@cs.stir.ac.uken_UK
dc.citation.date31/01/2018en_UK
dc.contributor.affiliationUniversity of Paduaen_UK
dc.contributor.affiliationJahangirnagar Universityen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of Paduaen_UK
dc.identifier.isiWOS:000432398300003en_UK
dc.identifier.scopusid2-s2.0-85041424010en_UK
dc.identifier.wtid497498en_UK
dc.contributor.orcid0000-0002-8080-082Xen_UK
dc.date.accepted2018-01-03en_UK
dcterms.dateAccepted2018-01-03en_UK
dc.date.filedepositdate2018-03-01en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionAMen_UK
local.rioxx.authorMahmud, Mufti|en_UK
local.rioxx.authorKaiser, Mohammed Shamim|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.authorVassanelli, Stefano|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2018-03-01en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2018-03-01|en_UK
local.rioxx.filename1711.03985.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source2162-2388en_UK
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