Taxonomic Classification for Living Organisms Using Convolutional Neural Networks
dc.contributor.author
dc.date.accessioned
2017-12-13T07:37:36Z
dc.date.available
2017-12-13T07:37:36Z
dc.date.issued
2017-11-17
dc.identifier.uri
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
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
MDPI (Multidisciplinary Digital Publishing Institute)
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.3390/genes8110326
dc.relation.ispartof
Genes, 2017, vol. 8, núm. 11, p. 326-337
dc.relation.ispartofseries
Articles publicats (D-ATC)
dc.rights
Attribution 3.0 Spain
dc.rights.uri
dc.subject
dc.title
Taxonomic Classification for Living Organisms Using Convolutional Neural Networks
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.eissn
2073-4425