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.
authors.affiliation: Advanced Virtual and Intelligent Computing (AVIC) Research Center Department of Mathematics and Computer Science, Chulalongkorn University Bangkok, Thailand. Electronic address: khantharat@gmail.com.
authors.forename: Khantharat
authors.initials: K
authors.lastname: Anekboon
authors.affiliation: Advanced Virtual and Intelligent Computing (AVIC) Research Center Department of Mathematics and Computer Science, Chulalongkorn University Bangkok, Thailand. Electronic address: lchidcha@chula.ac.th.
authors.forename: Chidchanok
authors.initials: C
authors.lastname: Lursinsap
authors.affiliation: Advanced Virtual and Intelligent Computing (AVIC) Research Center Department of Mathematics and Computer Science, Chulalongkorn University Bangkok, Thailand. Electronic address: suphakant.p@chula.ac.th.
authors.forename: Suphakant
authors.initials: S
authors.lastname: Phimoltares
authors.affiliation: Institute of Science and Technology for Research and Development Mahidol University, Nakhonpathom, Thailand. Electronic address: grsfc@mahidol.ac.th.
authors.forename: Suthat
authors.initials: S
authors.lastname: Fucharoen
authors.affiliation: Genome Institute, National Center for Genetic Engineering and Biotechnology, Pathumtani, Thailand. Electronic address: sissades@biotec.or.th.
authors.forename: Sissades
authors.initials: S
authors.lastname: Tongsima
mesh_terms: D000465:Algorithms; D003424:Crohn Disease / Q000235:genetics*; D030561:Databases, Nucleic Acid; D006801:Humans; D016571:Neural Networks, Computer; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods*
keywords: Association study; Significant feature identification; Single nucleotide polymorphism