Abstract
This work proposes a methodology to identify genes highly related with cancer. In particular, a multi-objective evolutionary algorithm 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.
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Acknowledgments
The financial support from the Spanish Ministry of Science and Technology, projects TIN2011-28956-C02-02 and TIN2014-55894-C2-1-R, and from the Junta de Andalucia, P11-TIC-7528 and P12-TIC-1728, is acknowledged.
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Medina, A.S., Pichardo, A.G., García-Heredia, J.M., Martínez-Ballesteros, . (2016). Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_58
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DOI: https://doi.org/10.1007/978-3-319-32034-2_58
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