dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Romero Zaliz, Rocío | es |
dc.creator | Cordón, Óscar | es |
dc.creator | Harari, Óscar | es |
dc.creator | Val, Coral del | es |
dc.creator | Zwir, Igor | es |
dc.date.accessioned | 2022-12-01T10:30:13Z | |
dc.date.available | 2022-12-01T10:30:13Z | |
dc.date.issued | 2006 | |
dc.identifier.citation | Rubio Escudero, C., Romero Zaliz, R., Cordón, Ó., Harari, Ó., Val, C.d. y Zwir, I. (2006). Optimal Selection of Microarray Analysis Methods Using a Conceptual Clustering Algorithm. En EvoWorkshops 2006: Workshops on Applications of Evolutionary Computation (172-183), Budapest, Hungary: Springer. | |
dc.identifier.isbn | 978-3-540-33237-4 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/139997 | |
dc.description.abstract | The rapid development of methods that select over/under expressed
genes from microarray experiments have not yet matched the need for tools that
identify informational profiles that differentiate between experimental condi tions such as time, treatment and phenotype. Uncertainty arises when methods
devoted to identify significantly expressed genes are evaluated: do all microar ray analysis methods yield similar results from the same input dataset? do dif ferent microarray datasets require distinct analysis methods?. We performed a
detailed evaluation of several microarray analysis methods, finding that none of
these methods alone identifies all observable differential profiles, nor subsumes
the results obtained by the other methods. Consequently, we propose a proce dure that, given certain user-defined preferences, generates an optimal suite of
statistical methods. These solutions are optimal in the sense that they constitute
partial ordered subsets of all possible method-associations bounded by both, the
most specific and the most sensitive available solution. | es |
dc.format | application/pdf | es |
dc.format.extent | 12 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | EvoWorkshops 2006: Workshops on Applications of Evolutionary Computation (2006), pp. 172-183. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Optimal Selection of Microarray Analysis Methods Using a Conceptual Clustering Algorithm | 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.publisherversion | https://link.springer.com/chapter/10.1007/11732242_16 | es |
dc.identifier.doi | 10.1007/11732242_16 | es |
dc.contributor.group | Universidad de Sevilla. TIC-254: Data Science and Big Data Lab | es |
dc.publication.initialPage | 172 | es |
dc.publication.endPage | 183 | es |
dc.eventtitle | EvoWorkshops 2006: Workshops on Applications of Evolutionary Computation | es |
dc.eventinstitution | Budapest, Hungary | es |
dc.relation.publicationplace | Berlin, Germany | es |