Supasak Kulawonganunchai

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Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these(More)
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from(More)
BACKGROUND Single nucleotide polymorphisms (SNPs) are the most commonly studied units of genetic variation. The discovery of such variation may help to identify causative gene mutations in monogenic diseases and SNPs associated with predisposing genes in complex diseases. Accurate detection of SNPs requires software that can correctly interpret chromatogram(More)
Congenital heart defects (CHD) occur in 40% of patients with trisomy 21, while the other 60% have a structurally normal heart. This suggests that the increased dosage of genes on chromosome 21 is a risk factor for abnormal heart development. Interaction of genes on chromosome 21 or their gene products with certain alleles of genes on other chromosomes could(More)
The Mycobacterium tuberculosis Beijing family is often associated with multidrug resistance and large outbreaks. Conventional genotyping study of a community outbreak of multidrug-resistant tuberculosis (MDR-TB) that occurred in Kanchanaburi Province, Thailand was carried out. The study revealed that the outbreak was clonal and the strain was identified as(More)
Finding gene interaction models is one of the most important issues in genotype-phenotype association studies. This paper presents a model-free nonparametric statistical interaction analysis known as Parallel Haplotype Configuration Reduction (pHCR). This technique extends the original Multifactor Dimensionality Reduction (MDR) algorithm by using haplotype(More)
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