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dc.contributor.authorCerruela García, Gonzalo
dc.contributor.authorLuque Ruiz, Irene
dc.contributor.authorGarcía Pedrajas, Nicolás
dc.contributor.authorGómez-Nieto, Miguel Ángel
dc.date.accessioned2018-10-17T12:19:46Z
dc.date.available2018-10-17T12:19:46Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10396/17331
dc.description.abstractIn this work we study and analyze the behavior of different representational spaces for molecular activity prediction. Representational spaces based on fingerprint similarity, structural similarity using maximum common subgraphs (MCS) and all maximum common subgraphs (AMCS) approaches are compared against representational spaces based on structural fragments and non-isomorphic fragments (NIF), built using different molecular descriptors. Support vector machine is used to study the influence of molecular representation in the dataset classification and PLS regression is proposed to construct a QSAR model for the molecular activity predictiones_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceEn: ESTYLF 2014, XVII Congreso Español sobre Tecnologías y Lógica Fuzzy, Zaragoza, 5-7, febrero, 2014, 363-368 (2014)es_ES
dc.subjectMolecular activity predictiones_ES
dc.subjectQSAR modelses_ES
dc.subjectSVMes_ES
dc.subjectPLS regressiones_ES
dc.titleEffects of Molecular Representation in Predicting the Biological Activity using SVM and PLS Approacheses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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