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SELECTION OF A MINIMAL NUMBER OF SIGNIFICANT PORCINE SNPs BY AN INFORMATION GAIN AND GENETIC ALGORITHM HYBRID MODEL
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Document Title
SELECTION OF A MINIMAL NUMBER OF SIGNIFICANT PORCINE SNPs BY AN INFORMATION GAIN AND GENETIC ALGORITHM HYBRID MODEL
Author
Rathasamuth W, Pasupa K, Tongsima S
Name from Authors Collection
Affiliations
King Mongkuts Institute of Technology Ladkrabang; National Science & Technology Development Agency - Thailand; National Center Genetic Engineering & Biotechnology (BIOTEC)
Type
Article
Source Title
MALAYSIAN JOURNAL OF COMPUTER SCIENCE
ISSN
0127-9084
Year
2019
Issue
1-2
Open Access
Green Submitted, Bronze
Publisher
UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH
DOI
10.22452/mjcs.sp2019no2.5
Format
Abstract
A 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.
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WOS