Publication: Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies
Issued Date
2014-01-01
Resource Type
ISSN
18790534
00104825
00104825
Other identifier(s)
2-s2.0-84888031937
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Mahidol University
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SCOPUS
Bibliographic Citation
Computers in Biology and Medicine. Vol.44, No.1 (2014), 57-65
Suggested Citation
Khantharat Anekboon, Chidchanok Lursinsap, Suphakant Phimoltares, Suthat Fucharoen, Sissades Tongsima Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies. Computers in Biology and Medicine. Vol.44, No.1 (2014), 57-65. doi:10.1016/j.compbiomed.2013.09.017 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/33758
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Title
Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies
Abstract
Crohn's disease is an inflammatory bowel disease. Because of strong heritability, it is possible to deploy the pattern of DNA variations, such as single nucleotide polymorphisms (SNPs), to accurately predict the state of this disease. However, there are many possible SNP subsets, which make finding a best set of SNPs to achieve the highest prediction accuracy impossible in one patient's lifetime. In this paper, a new technique is proposed that relies on chromosomes of various lengths with significant order feature selection, a new cross-over approach, and new mutation operations. Our method can find a chromosome of appropriate length with useful features. The Crohn's disease data that were gathered from case-control association studies were used to demonstrate the effectiveness of our proposed algorithm. In terms of the prediction accuracy, the proposed SNP prediction framework outperformed previously proposed techniques, including the optimum random forest (ORF), the univariate marginal distribution algorithm and support vector machine (USVM), the complimentary greedy search-based prediction algorithm (CGSP), the combinatorial search-based prediction algorithm (CSP), and discretized network flow (DNF). The performance of our framework, when tested against this real data set with a 5-fold cross-validation, was 90.4% accuracy with 87.5% sensitivity and 92.2% specificity. © 2013 Elsevier Ltd.