dc.creator | Nepomuceno Chamorro, Isabel de los Ángeles | es |
dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.creator | Díaz Díaz, Norberto | es |
dc.creator | Rodríguez Baena, Domingo S. | es |
dc.creator | García Gutiérrez, Jorge | es |
dc.date.accessioned | 2022-07-20T10:34:48Z | |
dc.date.available | 2022-07-20T10:34:48Z | |
dc.date.issued | 2007 | |
dc.identifier.citation | Nepomuceno Chamorro, I.d.l.Á., Aguilar Ruiz, J.S., Díaz Díaz, N., Rodríguez Baena, D.S. y García Gutiérrez, J. (2007). A Deterministic Model to Infer Gene Networks from Microarray Data. En IDEAL 2007: 8th International Conference on Intelligent Data Engineering and Automated Learning (850-859), Birmingham, UK: Springer. | |
dc.identifier.isbn | 978-3-540-77225-5 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/135639 | |
dc.description.abstract | Microarray experiments help researches to construct the str ucture of gene regulatory networks, i.e., networks representing relation ships among different genes. Filter and knowledge extraction processes
are necessary in order to handle the huge amount of data produced
by microarray technologies. We propose regression trees techniques as
a method to identify gene networks. Regression trees are a very use ful technique to estimate the numerical values for the target outputs.
They are very often more precise than linear regression models because
they can adjust different linear regressions to separate areas of the search
space. In our approach, we generate a single regression tree for each genes
from a set of genes, taking as input the remaining genes, to finally build
a graph from all the relationships among output and input genes. In this
paper, we will simplify the approach by setting an only seed, the gene
ARN1, and building the graph around it. The final model might gives
some clues to understand the dynamics, the regulation or the topology
of the gene network from one (or several) seeds, since it gathers rele vant genes with accurate connections. The performance of our approach
is experimentally tested on the yeast Saccharomyces cerevisiae dataset
(Rosetta compendium). | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | IDEAL 2007: 8th International Conference on Intelligent Data Engineering and Automated Learning (2007), pp. 850-859. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | A Deterministic Model to Infer Gene Networks from Microarray Data | 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/10.1007/978-3-540-77226-2_85 | es |
dc.identifier.doi | 10.1007/978-3-540-77226-2_85 | es |
dc.contributor.group | Universidad de Sevilla. TIC134: Sistemas Informáticos | es |
dc.publication.initialPage | 850 | es |
dc.publication.endPage | 859 | es |
dc.eventtitle | IDEAL 2007: 8th International Conference on Intelligent Data Engineering and Automated Learning | es |
dc.eventinstitution | Birmingham, UK | es |
dc.relation.publicationplace | Berlin, Germany | es |
dc.identifier.sisius | 6516644 | es |