Ponencia
Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules
Autor/es | Sánchez Medina, Alejandro
Gil Pichardo, Alberto García Heredia, José Manuel Martínez Ballesteros, María del Mar |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos Universidad de Sevilla. Departamento de Bioquímica Vegetal y Biología Molecular |
Fecha de publicación | 2016 |
Fecha de depósito | 2022-04-25 |
Publicado en |
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ISBN/ISSN | 978-3-319-32033-5 0302-9743 |
Resumen | This work proposes a methodology to identify genes highly
related with cancer. In particular, a multi-objective evolutionary algo rithm named CANGAR is applied to obtain quantitative association
rules. This kind of rules ... This work proposes a methodology to identify genes highly related with cancer. In particular, a multi-objective evolutionary algo rithm named CANGAR is applied to obtain quantitative association rules. This kind of rules are used to identify dependencies between genes and their expression levels. Hierarchical cluster analysis, fold-change and review of the literature have been considered to validate the relevance of the results obtained. The results show that the reported genes are consistent with prior knowledge and able to characterize cancer colon patients. |
Agencias financiadoras | Ministerio de Ciencia Y Tecnología (MCYT). España Ministerio de Economía y Competitividad (MINECO). España Junta de Andalucía |
Identificador del proyecto | TIN2011-28956-C02-02
TIN2014-55894-C2-1-R P11-TIC-7528 P12-TIC-1728 |
Cita | Sánchez Medina, A., Gil Pichardo, A., García Heredia, J.M. y Martínez Ballesteros, M.d.M. (2016). Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules. En HAIS 2016 : 11th International Conference on Hybrid Artificial Intelligence Systems (694-705), Sevilla, España: Springer. |
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