Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/177265
Title: GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines
Author: Díez Díaz, Fidel
Sánchez Lasheras, Fernando
Moreno Aguado, Víctor
Moratalla Navarro, Ferran
Molina de la Torre, Antonio José
Martín Sánchez, Vicente
Keywords: Aprenentatge automàtic
Algorismes
Genètica
Machine learning
Algorithms
Genetica
Issue Date: 18-Mar-2021
Publisher: MDPI
Abstract: Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants in an individual's sample in order to find if any of these variants are linked to a particular trait. In the last two decades, GWAS have contributed to several new discoveries in the field of genetics. This research presents a novel methodology to which GWAS can be applied to. It is mainly based on two machine learning methodologies, genetic algorithms and support vector machines. The database employed for the study consisted of information about 370,750 single-nucleotide polymorphisms belonging to 1076 cases of colorectal cancer and 973 controls. Ten pathways with different degrees of relationship with the trait under study were tested. The results obtained showed how the proposed methodology is able to detect relevant pathways for a certain trait: in this case, colorectal cancer.
Note: Reproducció del document publicat a: https://doi.org/10.3390/math9060654
It is part of: Mathematics, 2021, vol. 9, num. 6
URI: http://hdl.handle.net/2445/177265
Related resource: https://doi.org/10.3390/math9060654
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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