Many studies have shown that there is a direct relationship between Single Nucleotide Polymorphisms (SNPs) and the appearance of complex diseases, such as Alzheimer’s or Parkinson’s. However, recent advances in the Study of the Complete Genome Association indicate that the relationship between SNPs and these diseases goes beyond a simple one-to-one relationship, that is, the appearance of multiple SNPs (epistasis) influences the appearance of these diseases. In this sense, this work proposes the application of the NSGA-II multi-objective algorithm for the detection of epistasis of multiple loci in a database with 31,341 SNPs. Moreover, a parallel study has been performed to reduce the execution time of this problem. Our implementation not only achieves a reasonable good parallel performance and scalability, but also its biological significance overcomes other approaches published in the literature.
CITATION STYLE
Gallego-Sánchez, D., Granado-Criado, J. M., Santander-Jiménez, S., Rubio-Largo, Á., & Vega-Rodríguez, M. A. (2017). Parallel multi-objective optimization for high-order epistasis detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10393 LNCS, pp. 523–532). Springer Verlag. https://doi.org/10.1007/978-3-319-65482-9_38
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