Selection of a minimal number of significant porcine snps by an information gain and genetic algorithm hybrid model

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dc.contributor.authorRathasamuth, Wanthanee
dc.contributor.authorPasupa, Kitsuchart
dc.contributor.authorTongsima, Sissades
dc.date.accessioned2026-08-06T10:22:42Z
dc.date.available2026-08-06T10:22:42Z
dc.date.issued2019-01-01
dc.description.abstractA panel of a large number of common Single Nucleotide Polymorphisms (SNPs) distributed across an entire porcine genome has been widely used to represent genetic variability of pigs. With the advent of SNP-array technology, a genome-wide genetic profile of a specimen can be easily observed. Among the large number of such variations, there exists a much smaller subset of the SNP panel that could equally be used to correctly identify the corresponding breed. This work presents a SNP selection heuristic that can still be used effectively in the breed classification. The features were selected by combining a filter method and a wrapper method-information gain method and genetic algorithm-plus a feature frequency selection step, while classification used a support vector machine. We were able to reduce the number of significant SNPs to 0.86 % of the total number of SNPs in a swine dataset with 94.80 % classification accuracy.
dc.identifier.citationMalaysian Journal of Computer Science, 2019(SpecialIssue2), 79-95, 2019
dc.identifier.doi10.22452/mjcs.sp2019no2.5
dc.identifier.issn01279084
dc.identifier.other2-s2.0-85085652683
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/9351
dc.sourceMalaysian Journal of Computer Science
dc.subjectBioinformatics
dc.subjectFeature selection
dc.subjectGenetic algorithm
dc.subjectInformation gain
dc.subjectSingle nucleotide polymorphisms
dc.subjectSupport vector machine
dc.subjectSwine
dc.titleSelection of a minimal number of significant porcine snps by an information gain and genetic algorithm hybrid model
dc.typeArticle

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