dc.creator | Harari, Óscar | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Zwir, Igor | es |
dc.date.accessioned | 2022-12-01T11:52:08Z | |
dc.date.available | 2022-12-01T11:52:08Z | |
dc.date.issued | 2007 | |
dc.identifier.citation | Harari, Ó., Rubio Escudero, C. y Zwir, I. (2007). Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization. En EvoBIO 2007: 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (68-77), Valencia, España: Springer. | |
dc.identifier.isbn | 978-3-540-71782-9 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/140017 | |
dc.description.abstract | A critical challenge of the postgenomic era is to understand how
genes are differentially regulated in and between genetic networks. The fact
that such co-regulated genes may be differentially regulated suggests that subtle
differences in the shared cis-acting regulatory elements are likely significant,
however it is unknown which of these features increase or reduce expression of
genes. In principle, this expression can be measured by microarray experi ments, though they incorporate systematic errors, and moreover produce a lim ited classification (e.g. up/down regulated genes). In this work, we present an
unsupervised machine learning method to tackle the complexities governing
gene expression, which considers gene expression data as one feature among
many. It analyzes features concurrently, recognizes dynamic relations and gen erates profiles, which are groups of promoters sharing common features. The
method makes use of multiobjective techniques to evaluate the performance of
profiles, and has a multimodal approach to produce alternative descriptions of
same expression target. We apply this method to probe the regulatory networks
governed by the PhoP/PhoQ two-component system in the enteric bacteria Es cherichia coli and Salmonella enterica. Our analysis uncovered profiles that
were experimentally validated, suggesting correlations between promoter regu latory features and gene expression kinetics measured by green fluorescent pro tein (GFP) assays. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología BIO2004-0270-E | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | EvoBIO 2007: 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (2007), pp. 68-77. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | 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 | BIO2004-0270-E | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-540-71783-6_7 | es |
dc.identifier.doi | 10.1007/978-3-540-71783-6_7 | es |
dc.contributor.group | Universidad de Sevilla. TIC-254: Data Science and Big Data Lab | es |
dc.publication.initialPage | 68 | es |
dc.publication.endPage | 77 | es |
dc.eventtitle | EvoBIO 2007: 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics | es |
dc.eventinstitution | Valencia, España | es |
dc.relation.publicationplace | Berlin, Germany | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |