TaeHyun Hwang

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Building reliable predictive models from multiple complementary genomic data for cancer study is a crucial step towards successful cancer treatment and a full understanding of the underlying biological principles. To tackle this challenging data integration problem, we propose a hypergraph-based learning algorithm called HyperGene to integrate microarray(More)
MOTIVATION To validate the candidate disease genes identified from high-throughput genomic studies, a necessary step is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of(More)
MOTIVATION Incorporating biological prior knowledge into predictive models is a challenging data integration problem in analyzing high-dimensional genomic data. We introduce a hypergraph-based semi-supervised learning algorithm called HyperPrior to classify gene expression and array-based comparative genomic hybridization (arrayCGH) data using biological(More)
MOTIVATION A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new(More)
Promising results were recently reported in utilizing network information in phenotype-similarity network and gene-interaction network with graph-based learning to derive new disease phenotype-gene associations. However, a more fundamental understanding of how the network information is relevant to disease phenotype-gene associations is lacking. In this(More)
MOTIVATION The availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. To provide a fundamental understanding of how the network information is relevant to phenotype-gene(More)
Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype-gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this(More)
Double-stranded salmon DNA (SDNA) was doped with doxorubicin hydrochloride drug molecules (DOX) to determine the binding between DOX and SDNA, and DOX optimum doping concentration in SDNA. SDNA thin films were prepared with various concentrations of DOX by drop-casting on oxygen plasma treated glass and quartz substrates. Fourier transform infrared (FTIR)(More)