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dc.creatorRomero Zaliz, Rocíoes
dc.creatorRubio Escudero, Cristinaes
dc.creatorZwir, Igores
dc.creatorVal, Coral deles
dc.date.accessioned2022-12-01T10:42:15Z
dc.date.available2022-12-01T10:42:15Z
dc.date.issued2010
dc.identifier.citationRomero Zaliz, R., Rubio Escudero, C., Zwir, I. y Val, C.d. (2010). Optimization of multi-classifiers for computational biology: application to gene finding and expression. Theoretical Chemistry Accounts, 125 (3-6), 599-611. https://doi.org/10.1007/s00214-009-0648-3.
dc.identifier.issn1432-881Xes
dc.identifier.issn1432-2234es
dc.identifier.urihttps://hdl.handle.net/11441/140000
dc.description.abstractGenomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome and their expression. We propose a multi-objective methodology to combine state-of-the-art algorithms into an aggregation scheme in order to obtain optimal methods’ aggregations. The results obtained show a major improvement in sensitivity when our methodology is compared to the performance of individual methods for gene finding and gene expression problems. The methodology proposed here is an automatic method generator, and a step forward to exploit all already existing methods, by providing alternative optimal methods’ aggregations to answer concrete queries for a certain biological problem with a maximized accuracy of the prediction. As more approaches are integrated for each of the presented problems, de novo accuracy can be expected to improve further.es
dc.description.sponsorshipMinisterio de Ciencia y Tecnología TIN2006-12879es
dc.description.sponsorshipJunta de Andalucía TIC-02788es
dc.formatapplication/pdfes
dc.format.extent13es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofTheoretical Chemistry Accounts, 125 (3-6), 599-611.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMultiobjectivees
dc.subjectGene findinges
dc.subjectGene expressiones
dc.titleOptimization of multi-classifiers for computational biology: application to gene finding and expressiones
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDTIN2006-12879es
dc.relation.projectIDTIC-02788es
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s00214-009-0648-3es
dc.identifier.doi10.1007/s00214-009-0648-3es
dc.contributor.groupUniversidad de Sevilla. TIC-254: Data Science and Big Data Labes
dc.journaltitleTheoretical Chemistry Accountses
dc.publication.volumen125es
dc.publication.issue3-6es
dc.publication.initialPage599es
dc.publication.endPage611es
dc.contributor.funderMinisterio de Ciencia Y Tecnología (MCYT). Españaes
dc.contributor.funderJunta de Andalucíaes

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