Abstract
Genome-wide association (GWA) studies aim to identify genetic variations (polymorphisms) associated with diseases, and more generally, with traits. Commonly, a Single Nucleotide Polymorphism (SNP) is considered as it is the most common form of genetic variations. In the literature, several statistical and data mining methods have been applied to GWA data analysis. In this article, we present a preliminary study where we examine the possibilities of applying biclustering approaches to detect association between SNP markers and phenotype traits. Therefore, we propose a multiobjective model for biclustering problems in GWA context. Furthermore, we propose an adapted heuristic and metaheuristic to solve it. The performance of our algorithms are assessed using synthetic data sets.
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Seridi, K., Jourdan, L., Talbi, EG. (2014). Preliminary Studies on Biclustering of GWA: A Multiobjective Approach. In: Legrand, P., Corsini, MM., Hao, JK., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2013. Lecture Notes in Computer Science(), vol 8752. Springer, Cham. https://doi.org/10.1007/978-3-319-11683-9_9
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DOI: https://doi.org/10.1007/978-3-319-11683-9_9
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