Research Interests

Our research interests are the assessment, improvement, and development of computational approaches, software applications and databases that will facilitate the management, integration and analysis of diverse sources of biomedical data.

In order to benefit human health, advances in the genomics area must be translated into improvements in clinical care. A better understanding of the underlying biological and environmental factors that influence disease ("bench") will lead to the development of practical applications to directly benefit the outcome of patient care ("bedside"). A key component of translational research is the management, integration and analysis of large quantities of data from both the biosciences and clinical domains.

Integrative Analysis of High Density SNP data sets

In the area of high throughput genomic data, our research is focused on several issues concerning the integration of biological domain knowledge, such as pathway information, to supplement statistical and data-mining algorithms to explore the etiology of complex diseases. Examples include an in-depth association analysis of the complement pathway single nucleotide polymorphisms (SNPs) with age related macular degeneration (AMD), and Pathway/SNP, a software tool that allows an exploratory approach to integrative association analysis.

Journal of Biomedical Informatics

BING: Biomedical informatics pipeline for Next Generation Sequencing. more...

Integrating domain knowledge with statistical and data mining methods for high density genomic SNP disease association analysis. more...

BMC Bioinformatics

ICRPfinder: a fast pattern design algorithm for coding sequences and its application in finding potential restriction enzyme recognition sites. more...

Genetic Epidemiology

Evidence for association between multiple complement pathway genes and AMD. more...