Inferring Gene-Gene Associations from Quantitative Association Rules

The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of gene...

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Bibliographic Details
Authors: Martínez Ballesteros, María del Mar, Nepomuceno Chamorro, Isabel de los Ángeles, Riquelme Santos, José Cristóbal
Format: book part
Status:Published version
Publication Date:2011
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/42186
Online Access:http://hdl.handle.net/11441/42186
https://doi.org/10.1109/ISDA.2011.6121829
Access Level:Open access
Keyword:Data mining
evolutionary algorithms
quantitative association rules
gene networks
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spelling Inferring Gene-Gene Associations from Quantitative Association RulesMartínez Ballesteros, María del MarNepomuceno Chamorro, Isabel de los ÁngelesRiquelme Santos, José CristóbalData miningevolutionary algorithmsquantitative association rulesgene networksThe microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number of experiments or samples available. Microarray experiments are generating datasets that can help in reconstructing gene networks. One of the most important problems in network reconstruction is finding, for each gene in the network, which genes can affect it and how. Association Rules are an approach of unsupervised learning to relate attributes to each other. In this work we use Quantitative Association Rules in order to define interrelations between genes. These rules work with intervals on the attributes, without discretizing the data before and they are generated by a multi-objective evolutionary algorithm. In most cases the extracted rules confirm the existing knowledge about cell-cycle gene expression, while hitherto unknown relationships can be treated as new hypotheses.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-02611IEEELenguajes y Sistemas InformáticosMinisterio de Ciencia y Tecnología (MCYT). EspañaJunta de Andalucía2011info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/42186https://doi.org/10.1109/ISDA.2011.6121829reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésProceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications 22 – 24 November 2011 Córdoba, SpainTIN2007-68084-C-00P07-TIC-02611info:eu-repo/semantics/openAccessoai:idus.us.es:11441/421862026-06-17T12:51:07Z
dc.title.none.fl_str_mv Inferring Gene-Gene Associations from Quantitative Association Rules
title Inferring Gene-Gene Associations from Quantitative Association Rules
spellingShingle Inferring Gene-Gene Associations from Quantitative Association Rules
Martínez Ballesteros, María del Mar
Data mining
evolutionary algorithms
quantitative association rules
gene networks
title_short Inferring Gene-Gene Associations from Quantitative Association Rules
title_full Inferring Gene-Gene Associations from Quantitative Association Rules
title_fullStr Inferring Gene-Gene Associations from Quantitative Association Rules
title_full_unstemmed Inferring Gene-Gene Associations from Quantitative Association Rules
title_sort Inferring Gene-Gene Associations from Quantitative Association Rules
dc.creator.none.fl_str_mv Martínez Ballesteros, María del Mar
Nepomuceno Chamorro, Isabel de los Ángeles
Riquelme Santos, José Cristóbal
author Martínez Ballesteros, María del Mar
author_facet Martínez Ballesteros, María del Mar
Nepomuceno Chamorro, Isabel de los Ángeles
Riquelme Santos, José Cristóbal
author_role author
author2 Nepomuceno Chamorro, Isabel de los Ángeles
Riquelme Santos, José Cristóbal
author2_role author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
Ministerio de Ciencia y Tecnología (MCYT). España
Junta de Andalucía
dc.subject.none.fl_str_mv Data mining
evolutionary algorithms
quantitative association rules
gene networks
topic Data mining
evolutionary algorithms
quantitative association rules
gene networks
description The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number of experiments or samples available. Microarray experiments are generating datasets that can help in reconstructing gene networks. One of the most important problems in network reconstruction is finding, for each gene in the network, which genes can affect it and how. Association Rules are an approach of unsupervised learning to relate attributes to each other. In this work we use Quantitative Association Rules in order to define interrelations between genes. These rules work with intervals on the attributes, without discretizing the data before and they are generated by a multi-objective evolutionary algorithm. In most cases the extracted rules confirm the existing knowledge about cell-cycle gene expression, while hitherto unknown relationships can be treated as new hypotheses.
publishDate 2011
dc.date.none.fl_str_mv 2011
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/42186
https://doi.org/10.1109/ISDA.2011.6121829
url http://hdl.handle.net/11441/42186
https://doi.org/10.1109/ISDA.2011.6121829
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications 22 – 24 November 2011 Córdoba, Spain
TIN2007-68084-C-00
P07-TIC-02611
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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