title: Application of LogitBoost Classifier for Traceability Using SNP Chip Data.
abstract: Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To accomplish this, 4,122 pigs originating from 104 farms were genotyped using the SNP chip. Several factors were considered to establish the best prediction model based on these data. We also assessed the applicability of the suggested model using a kinship coefficient-filtering approach. Our results showed that the LogitBoost-based prediction model outperformed other classifiers in terms of classification performance under most conditions. Specifically, a greater level of accuracy was observed when a higher kinship-based cutoff was employed. These results demonstrated the applicability of a machine learning-based approach using SNP chip data for practical traceability.
authors.affiliation: Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151-921, Republic of Korea; C&K Genomics Inc., 514 Main Bldg., Seoul National University Research Park, San 4-2 Bongcheon-dong, Gwanak-gu, Seoul 151-919, Republic of Korea.
authors.forename: Kwondo
authors.initials: K
authors.lastname: Kim
authors.affiliation: C&K Genomics Inc., 514 Main Bldg., Seoul National University Research Park, San 4-2 Bongcheon-dong, Gwanak-gu, Seoul 151-919, Republic of Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Republic of Korea.
authors.forename: Minseok
authors.initials: M
authors.lastname: Seo
authors.affiliation: Department of Animal Science and Technology, College of Life Science and Natural Resources, Sunchon National University, Suncheon, 540-742, Republic of Korea.
authors.forename: Hyunsung
authors.initials: H
authors.lastname: Kang
authors.affiliation: C&K Genomics Inc., 514 Main Bldg., Seoul National University Research Park, San 4-2 Bongcheon-dong, Gwanak-gu, Seoul 151-919, Republic of Korea.
authors.forename: Seoae
authors.initials: S
authors.lastname: Cho
authors.affiliation: Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151-921, Republic of Korea; C&K Genomics Inc., 514 Main Bldg., Seoul National University Research Park, San 4-2 Bongcheon-dong, Gwanak-gu, Seoul 151-919, Republic of Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Republic of Korea.
authors.forename: Heebal
authors.initials: H
authors.lastname: Kim
authors.affiliation: Department of Animal Science and Technology, College of Life Science and Natural Resources, Sunchon National University, Suncheon, 540-742, Republic of Korea.
authors.forename: Kang-Seok
authors.initials: KS
authors.lastname: Seo
mesh_terms: D000822:Animal Husbandry / Q000379:methods; D000823:Animal Identification Systems; D000818:Animals; D019540:Area Under Curve; D015982:Bias; D001947:Breeding; D003198:Computer Simulation; D059022:Food Safety / Q000379:methods*; D005828:Genetics, Population; D005838:Genotype; D008460:Meat; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D018401:Sample Size; D012494:Sampling Studies; D034421:Sus scrofa / Q000145:classification / Q000235:genetics*