Subareas: Biostatistics, Computational Genetics, Proteomics, Statistical Genetics

We have worked on a wide range of cutting edge problems in the area of bioinformatics and computational biology.

Computational Genetics:  Advances have been made over the last decade in our understanding of how genes influence phenotypes and contribute to disease susceptibility. It has become increasingly clear that the underlying mechanisms have a complex basis in which observed clinical outcomes result from a diverse range of causes interconnected through networks of genetic, biological and environmental interactions. The advances in high-throughput genotyping and sequencing have generated massive amounts of data that allow genome-wide analysis to be performed at much finer resolution than before but at the same time posed great computational challenges. We have investigated a wide range of problems including haplotype inference, imputation, genome-wide association study, alternative splicing analysis, copy number variation detection, methylation, genome annotation and visualization.

Immunology, Development and Differentiation, and Metagenomics: We use novel measurement techniques as well as machine learning methods in understanding the interplay between these areas, with the aim of discovering the forces that shape the immune system throughout life. The overarching goal is to apply the insights from such analyses to propose new treatments for cancers.

Molecular Structure Modeling and Analysis: Diverse biological function is encoded in the atomic structure of macro-molecules such as Proteins and RNA. Understanding the sequence to structure to function relationships allows biochemists to predict the activity of genes and rationally design genes with novel biological function. Our interests include computational geometry models for molecular structure, high performance computing for dynamic simulation, mining structure motifs for protein functional prediction, remote homology detection, protein-protein interaction, and protein-ligand interaction.