Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies
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 fin...
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th-mahidol.337582018-11-09T09:57:29Z Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies Khantharat Anekboon Chidchanok Lursinsap Suphakant Phimoltares Suthat Fucharoen Sissades Tongsima Chulalongkorn University Mahidol University Thailand National Center for Genetic Engineering and Biotechnology Computer Science Medicine 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. 2018-11-09T02:11:36Z 2018-11-09T02:11:36Z 2014-01-01 Article Computers in Biology and Medicine. Vol.44, No.1 (2014), 57-65 10.1016/j.compbiomed.2013.09.017 18790534 00104825 2-s2.0-84888031937 https://repository.li.mahidol.ac.th/handle/123456789/33758 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84888031937&origin=inward |
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Computer Science Medicine 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 |
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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. |
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Chulalongkorn University |
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Chulalongkorn University Khantharat Anekboon Chidchanok Lursinsap Suphakant Phimoltares Suthat Fucharoen Sissades Tongsima |
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Khantharat Anekboon Chidchanok Lursinsap Suphakant Phimoltares Suthat Fucharoen Sissades Tongsima |
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Khantharat Anekboon |
title |
Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies |
title_short |
Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies |
title_full |
Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies |
title_fullStr |
Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies |
title_full_unstemmed |
Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies |
title_sort |
extracting predictive snps in crohn's disease using a vacillating genetic algorithm and a neural classifier in case-control association studies |
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2018 |
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https://repository.li.mahidol.ac.th/handle/123456789/33758 |
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1763497678950367232 |