BitQT: a graph-based approach to the quality threshold clustering of molecular dynamics

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datacite.creatorGonzález-Alemán, Roy
datacite.creatorPlatero-Rochart, Daniel
datacite.creatorHernández-Castillo, David
datacite.creatorHernández-Rodriguez, Erix W.
datacite.creatorCaballero, Julio
datacite.creatorLeclerc, Fabrice
datacite.creatorMontero-Cabrera, Luis
datacite.date.issued2022
datacite.identifierDOI
datacite.identifier.doi10.1093/bioinformatics/btab595
datacite.identifier.issn1367-4803
datacite.identifier.orcid0000-0003-3852-4902
datacite.identifier.orcid0000-0001-6454-4320
datacite.identifier.orcid0000-0002-2646-1644
datacite.identifier.orcid0000-0002-9231-7552
datacite.identifier.orcid0000-0003-0182-1444
datacite.identifier.orcid0000-0002-5641-1525
datacite.identifier.orcid0000-0002-4128-1203
datacite.identifier.wosidWOS:000736120000011
datacite.rightsAcceso Libre
datacite.subjectMaximum Clique Algorithm
datacite.subjectSimulation
datacite.subjectProgram
datacite.subjectArtmap
datacite.titleBitQT: a graph-based approach to the quality threshold clustering of molecular dynamics
dc.date.accessioned2024-11-08T12:34:32Z
dc.date.available2024-11-08T12:34:32Z
dc.description.abstractMotivation: Classical Molecular Dynamics (MD) is a standard computational approach to model time-dependent processes at the atomic level. The inherent sparsity of increasingly huge generated trajectories demands clustering algorithms to reduce other post-simulation analysis complexity. The Quality Threshold (QT) variant is an appealing one from the vast number of available clustering methods. It guarantees that all members of a particular cluster will maintain a collective similarity established by a user-defined threshold. Unfortunately, its high computational cost for processing big data limits its application in the molecular simulation field. Results: In this work, we propose a methodological parallel between QT clustering and another well-known algorithm in the field of Graph Theory, the Maximum Clique Problem. Molecular trajectories are represented as graphs whose nodes designate conformations, while unweighted edges indicate mutual similarity between nodes. The use of a binary-encoded RMSD matrix coupled to the exploitation of bitwise operations to extract clusters significantly contributes to reaching a very affordable algorithm compared to the few implementations of QT for MD available in the literature. Our alternative provides results in good agreement with the exact one while strictly preserving the collective similarity of clusters.
dc.description.pages6 p.
dc.identifier.urihttps://repositorio.utalca.cl/repositorio/handle/1950/14453
dc.languageInglés
dc.publisherOxford Univ. Press
dc.relation.urihttps://academic-oup-com.utalca.idm.oclc.org/bioinformatics/article/38/1/73/6353027
dc.sourceBioinformatics
oaire.citationTitleBioinformatics
oaire.licenseConditionhttps://creativecommons.org/licenses/by-sa/4.0/deed.es
oaire.licenseCondition.urihttps://creativecommons.org/licenses/by-sa/4.0/deed.es
oaire.resourceTypeArtículo
oaire.versionVersión Publicada
utalca.catalogadorAGA
utalca.facultadUniversidad de Talca (Chile). Facultad de Ingeniería. Departamento de Bioinformática.
utalca.idcargaaga081124
utalca.indexArtículo indexado en Web of Science
utalca.indexArtículo indexado en Scopus
utalca.informaciondegeneroHombre
utalca.odsIndustria, innovación e infraestructura
utalca.odsProducción y consumo responsables
utalca.odsEducación de calidad
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