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dc.contributor.authorCerruela García, Gonzalo
dc.contributor.authorCuevas-Muñoz, José Manuel
dc.contributor.authorGarcía Pedrajas, Nicolás
dc.date.accessioned2024-04-22T12:11:36Z
dc.date.available2024-04-22T12:11:36Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/10396/27983
dc.description.abstractIn the construction of QSAR models for the prediction of molecular activity, feature selection is a common task aimed at improving the results and understanding of the problem. The selection of features allows elimination of irrelevant and redundant features, reduces the effect of dimensionality problems, and improves the generalization and interpretability of the models. In many feature selection applications, such as those based on ensembles of feature selectors, it is necessary to combine different selection processes. In this work, we evaluate the application of a new feature selection approach to the prediction of molecular activity, based on the construction of an undirected graph to combine base feature selectors. The experimental results demonstrate the efficiency of the graph-based method in terms of the classification performance, reduction, and redundancy compared to the standard voting method. The graph-based method can be extended to different feature selection algorithms and applied to other cheminformatics problems.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherAmerican Chemical Societyes_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceCerruela-García, G., Cuevas-Muñoz, J. M., & García-Pedrajas, N. (2022). Graph-based feature selection approach for molecular activity prediction. Journal of Chemical Information and Modeling, 62(7), 1618–1632. https://doi.org/10.1021/acs.jcim.1c01578es_ES
dc.subjectMolecular activityes_ES
dc.subjectPredictiones_ES
dc.subjectQSAR modelses_ES
dc.subjectGraph-based methodes_ES
dc.titleGraph-Based Feature Selection Approach for Molecular Activity Predictiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1021/acs.jcim.1c01578es_ES
dc.relation.projectIDGobierno de España. PID2019-109481GB-I00/AEI/10.13039/501100011033es_ES
dc.relation.projectIDJunta de Andalucía. UCO-1264182es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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