Associate Professor of Bioinformatics
Department of Microbiology and Immunology
Jacobs School of Medicine & Biomedical Sciences
Artificial Intelligence; Bioinformatics; Biomedical Informatics; Computational Biology; Gene Expression
The recent development of high-throughput genomics technologies is revolutionizing many aspects of modern biology. However, the lack of computational algorithms and resources for analyzing massive data generated by these techniques has become a rate-limiting factor for scientific discoveries in biology research.
In my laboratory, we study machine learning, data mining and bioinformatics and their applications to cancer informatics and metagenomics. Our work is based on solid mathematical and statistical theories. The main focus of our research is on developing advanced algorithms to help biologists keep pace with the unprecedented growth of genomics datasets available today and enable them to make full use of their massive, high-dimensional data for various biological enquiries.
My research team is working on two major projects. The first is focused on metagenomics, currently funded by the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Women’s Health Initiative. Our goal is to develop an integrated suite of computational and statistical algorithms to process millions or even hundreds of millions of microbial genome sequences to: 1) derive quantitative microbial signatures to characterize various infectious diseases, 2) interactively visualize the complex structure of a microbial community, 3) study microbe-microbe interactions and community dynamics and 4) identify novel species. We collaborate with researchers throughout the University at Buffalo, notably those in the School of Medicine and Biomedical Sciences, the School of Public Health and Health Professions and the College of Arts and Sciences.
The second project focuses on cancer progression modeling. We use advanced computational algorithms to integrate clinical and genetics data from thousands of tumor and normal tissue samples to build a model of cancer progression. Delineating the disease dynamic process and identifying the molecular events that drive stepwise progression to malignancy would provide a wealth of new insights. Results of this work also would guide the development of improved cancer diagnostics, prognostics and targeted therapeutics.
The bioinformatics algorithms and software developed in our lab have been used by more than 200 research institutes worldwide to process large, complex data sets that are core to a wide variety of biological and biomedical research.