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dc.contributor.authorBarbero Aparicio, José Antonio 
dc.contributor.authorCuesta López, Santiago 
dc.contributor.authorGarcía Osorio, César 
dc.contributor.authorPérez-Rodríguez, Javier
dc.contributor.authorGarcía-Pedrajas, Nicolás
dc.date.accessioned2023-01-26T12:32:03Z
dc.date.available2023-01-26T12:32:03Z
dc.date.issued2022-12
dc.identifier.urihttp://hdl.handle.net/10259/7337
dc.description.abstractThere is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction. In this article, one such model is used as an additional information source that, when used by a machine learn‑ ing (ML) model, improves the results of current methods for the prediction of TSSs. In addition, we provide evidence on the validity of the physical model, as it is able by itself to predict TSSs with high accuracy. This opens an exciting avenue of research at the intersection of statistical mechanics and ML, where ML models in bioinformatics can be improved using physical models of DNA as feature extractors.es
dc.description.sponsorshipThis work has been supported by the Junta de Andalucia under project UCO1264182 and by the Ministry of Science, Innovation and Universities under project PID2019-109481GB-I00/AEI/q10.13039/501100011033, in both cases co-financed through European Union FEDER funds. José A. Barbero-Aparicio is founded through a predoctoral grant from the University of Burgos.es
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherSpringer Naturees
dc.relation.ispartofBMC Bioinformatics. 2022, V. 23, n. 1, 565es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectDNA modellinges
dc.subjectDNA breathinges
dc.subjectMachine learninges
dc.subjectTSS predictiones
dc.subjectSVMes
dc.subjectString kernelses
dc.subject.otherInformáticaes
dc.subject.otherComputer sciencees
dc.titleNonlinear physics opens a new paradigm for accurate transcription start site predictiones
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1186/s12859-022-05129-4es
dc.identifier.doi10.1186/s12859-022-05129-4
dc.relation.projectIDinfo:eu-repo/grantAgreement/Junta de Andalucía//UCO-1264182/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109481GB-I00/ES/NUEVA APROXIMACION A LA CONSTRUCCION DE ENJAMBRES PARA APRENDIZAJE MULTI-ETIQUETA: APLICACION A LA QUEMINFORMATICA Y LA BIOINFORMATICA/es
dc.identifier.essn1471-2105
dc.journal.titleBMC Bioinformaticses
dc.volume.number23es
dc.issue.number1es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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