Taxonomic classification for living organisms using convolutional neural networks

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Khawaldeh, Saed Pervaiz, Usama Elsharnoby, Mohammed Alchalabi, Alaa Eddin Al-Zubi, Nayel 2018-02-09T09:52:51Z 2018-02-09T09:52:51Z 2017-11-17
dc.identifier.citation Khawaldeh , S , Pervaiz , U , Elsharnoby , M , Alchalabi , A E & Al-Zubi , N 2017 , ' Taxonomic classification for living organisms using convolutional neural networks ' Genes , vol 8 , no. 11 , 326 . DOI: 10.3390/genes8110326 en
dc.identifier.issn 2073-4425
dc.identifier.other PURE UUID: 0931f6e8-6bf0-4f5c-bac4-3aef19d60d77
dc.identifier.other PURE ITEMURL:
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dc.description.abstract Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Genes en
dc.relation.ispartofseries Volume 8, issue 11 en
dc.rights openAccess en
dc.subject.other Genetics en
dc.subject.other Genetics(clinical) en
dc.subject.other 113 Computer and information sciences en
dc.title Taxonomic classification for living organisms using convolutional neural networks en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Aalto University
dc.contributor.department Universite de Bourgogne
dc.contributor.department Istanbul Sehir University
dc.contributor.department Al-Balqa Applied University
dc.contributor.department Department of Electrical Engineering and Automation en
dc.subject.keyword Convolutional neural networks
dc.subject.keyword DNA
dc.subject.keyword Encoding
dc.subject.keyword Genes
dc.subject.keyword Taxonomic classification
dc.subject.keyword Genetics
dc.subject.keyword Genetics(clinical)
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201802091221
dc.identifier.doi 10.3390/genes8110326
dc.type.version publishedVersion

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