Vitara Pungpapong

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Altered branching and aberrant expression of N-linked glycans is known to be associated with disease states such as cancer. However, the complexity of determining such variations hinders the development of specific glycomic approaches for assessing disease states. Here, we examine a combination of ion mobility spectrometry (IMS) and mass spectrometry (MS)(More)
Genome-wide associations between single-nucleotide polymorphisms and clinical traits were simultaneously conducted using penalized orthogonal-components regression. This method was developed to identify the genetic variants controlling phenotypes from a massive number of candidate variants. By investigating the association between all single-nucleotide(More)
Genome-wide association studies have successfully identified numerous loci at which common variants influence disease risks or quantitative traits of interest. Despite these successes, the variants identified by these studies have generally explained only a small fraction of the variations in the phenotype. One explanation may be that many rare variants(More)
Recent advances in high-throughput genotyping have motivated genomic selection using high-density markers. However, an increasingly large number of markers brings up both statistical and computational issues and makes it difficult to estimate the breeding values. We propose to apply the penalized orthogonal-components regression (POCRE) method to estimate(More)
Empirical Bayes methods are privileged in data mining because they can absorb prior information on model parameters and are free of choosing tuning parameters. We proposed an iterated conditional modes/medians (ICM/M) algorithm to implement empirical Bayes selection of massive variables while incorporating sparsity or more complicated a priori information.(More)
Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible(More)
Currently, genome-wide association studies (GWAS) are conducted by collecting a massive number of SNPs (i.e., large p) for a relatively small number of individuals (i.e., small n) and associations are made between clinical phenotypes and genetic variation one single-nucleotide polymorphism (SNP) at a time. Univariate association approaches like this ignore(More)
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