Our Team

Research

Dr. Cheung’s research has been focusing on the development and application of mathematical, statistical, and computational pattern recognition methods for recognition of gene promoters and for analysis of different aspects of gene expression profiles. He has been developing his own research program in Bioinformatics and Computational Biology for Cancer Genomics and Proteomics. His major research interests consist of three research streams. The first research stream is concerned with simple and compound pattern/motif recognition and data mining in “omics” to help understand more about gene expression, gene-gene and gene-environment interactions at different levels. Specific research includes mathematical & statistical investigations of multiple sequence patterns/motifs or molecular signatures/meta-signatures and their joint distributions in genomes, transcriptomes and proteomes, statistical analysis of regulatory sequences (e.g. promoters, enhancers, etc.) controlling the expression of genes, statistical & computational knowledge discovery on alternative splicing mechanism, gene network inference from the analysis of DNA microarray data, and biopathway modeling & simulation. Dr. Cheung received a SUN Microsystems Academic Equipment Grant for Bioinformatics on “Statistical and Computational Approaches to Genomics and Proteomics” and CRCH developmental funds on “Statistical Pattern Recognition Methods for Profiling Gene Expression Patterns”. He has been also a co-principal investigator on a Department of Defense funded Nutrigenomics project with Dr. Loic Le Marchand at CRCH on “Nutrition and Cancer: A Study on the Effects of Cancer-Preventive Foods on Genome-Wide Gene Expression”. The second research stream is concerned with genetic epidemiology and genetic mapping through linkage analysis and association studies. Specific research includes Bayesian statistical methods and machine-learning techniques for whole genome association studies based on haplotype-tagging single nucleotide polymorphisms (ht-SNPs) data, mapping quantitative trait loci (QTL) and dissecting complex polygenic architectures of quantitative traits. The third research stream is concerned with mathematical & statistical biophysical studies on spatial structures, geometry and dynamics of interacting biomolecules. Specific research includes probabilistic modeling and molecular dynamics simulations of DNA, RNA (especially micro-RNA) and proteins. Facing the challenges in these individually exciting research topics, Dr. Cheung is currently working on a novel multidimensional class of probabilistic models that can be used as a new analytical tool to incorporate multiple sources of different kinds of data to help understand and investigate various aspects of human health and the development of many complex diseases in an integrated fashion. The application focus of this work is on studying biological processes and biochemical pathways related to programmed cell death or apoptosis and the development of various tumors and cancers.

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