dc.creator | Romero Zaliz, Rocío | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Zwir, Igor | es |
dc.creator | Val, Coral del | es |
dc.date.accessioned | 2022-12-01T10:42:15Z | |
dc.date.available | 2022-12-01T10:42:15Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Romero 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.issn | 1432-881X | es |
dc.identifier.issn | 1432-2234 | es |
dc.identifier.uri | https://hdl.handle.net/11441/140000 | |
dc.description.abstract | Genomes 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.sponsorship | Ministerio de Ciencia y Tecnología TIN2006-12879 | es |
dc.description.sponsorship | Junta de Andalucía TIC-02788 | es |
dc.format | application/pdf | es |
dc.format.extent | 13 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Theoretical Chemistry Accounts, 125 (3-6), 599-611. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Multiobjective | es |
dc.subject | Gene finding | es |
dc.subject | Gene expression | es |
dc.title | Optimization of multi-classifiers for computational biology: application to gene finding and expression | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2006-12879 | es |
dc.relation.projectID | TIC-02788 | es |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s00214-009-0648-3 | es |
dc.identifier.doi | 10.1007/s00214-009-0648-3 | es |
dc.contributor.group | Universidad de Sevilla. TIC-254: Data Science and Big Data Lab | es |
dc.journaltitle | Theoretical Chemistry Accounts | es |
dc.publication.volumen | 125 | es |
dc.publication.issue | 3-6 | es |
dc.publication.initialPage | 599 | es |
dc.publication.endPage | 611 | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |
dc.contributor.funder | Junta de Andalucía | es |