Research

The focus of our research is to develop novel computational methodsfor the analysis of data from high-throughput molecular biology experiments. With the arrival of high-throughput data sources such as expression arrays, biologists have begun to depend upon computational tools to process and present biological data to enable inferences. New sources of information complementary to expression arrays are now becoming available and will permit biologists to gain substantial new insights into the functioning of biological systems. For example, location analysis is a high-throughput methodology for discovering where transcriptional regulators are bound to the genome under a desired condition.

The specific focus of our research project is the substantial computational challenges associated with utilizing complementary data sources in a principled manner. The biologists we collaborate with already recognize that new computational methods for fusing data from disparate assays will be essential to make subsantial progress in discovering genetic regulatory networks.

Unique strengths of our effort are a strong interdisciplinary team, our access to unique location data that promises to be an essential counterpoint to expression data, momentum with students, and the foundation provided by our previous research in data normalization, optimal clustering, time-series data processing, and modeling expression data with graphical models.

Please take a look at our publications page for details on our work.