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Generation of artificial neural networks models in anticancer study

2013Journal article Open access
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dc.contributor.authorSousa, Inês J.
dc.contributor.authorPadrón, José M.
dc.contributor.authorFernandes, Miguel X.
dc.date.accessioned2023-02-10T14:31:39Z
dc.date.available2023-02-10T14:31:39Z
dc.date.issued2013
dc.description.abstractArtificial neural networks (ANNs) have several applications; one of them is the prediction of biological activity. Here, ANNs were applied to a set of 32 compounds with anticancer activity assayed experimentally against two cancer cell lines (A2780 and T-47D). Using training and test sets, the obtained correlation coefficients between experimental and calculated values of activity, for A2780, were 0.804 and 0.829, respectively, and for T-47D, we got 0.820 for the training set and 0.927 for the test set. Com paring multiple linear regression and ANN models, the latter were better suited in establishing relationships between compounds’ structure and their anticancer activity.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSousa, I. J., Padrón, J. M., & Fernandes, M. X. (2013). Generation of artificial neural networks models in anticancer study. Neural Computing and Applications, 23, 577-582.pt_PT
dc.identifier.doi10.1007/s00521-013-1404-0pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5026
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationStrategic Project - UI 674 - 2011-2012
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBackpropagation algorithmpt_PT
dc.subjectCorrelation coefficientspt_PT
dc.subjectHeuristicspt_PT
dc.subjectLearning algorithmspt_PT
dc.subjectMachine learningpt_PT
dc.subjectNeural network modelspt_PT
dc.subjectNonlinear modelspt_PT
dc.subjectPrediction methodspt_PT
dc.subjectRadial base function networkpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleGeneration of artificial neural networks models in anticancer studypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleStrategic Project - UI 674 - 2011-2012
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/PEst-OE%2FQUI%2FUI0674%2F2011/PT
oaire.citation.endPage582pt_PT
oaire.citation.issue3-4pt_PT
oaire.citation.startPage577pt_PT
oaire.citation.titleNeural Computing and Applicationspt_PT
oaire.citation.volume23pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFernandes
person.givenNameMiguel Xavier
person.identifier.ciencia-idED1D-3C7A-467C
person.identifier.orcid0000-0002-1840-616X
person.identifier.ridA-4373-2013
person.identifier.scopus-author-id35466972500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscovery8dab9a0d-f44a-4d2d-b9b1-7b3145162ca3
relation.isProjectOfPublication595c1582-33c0-493a-8ab3-37d2811536d9
relation.isProjectOfPublication.latestForDiscovery595c1582-33c0-493a-8ab3-37d2811536d9

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