Evolutionary Medicine and Informatics: Discovery Bioinformatics
Modeling, Analysis, and Simulations to Discover Patterns and Test Predictions
Today, we can produce more DNA sequence and biological image data in a month than was previously possible over the course of a year. In only a few years, we’ve gone from one complete human genome sequence to dozens, with thousands on the immediate horizon. As our ability to collect biological sequence and image data expands by leaps and bounds—whether by high-throughput genome sequencing or large-scale image acquisition—new approaches are needed to store, mine, and analyze this unprecedented volume of information.
Our research encompasses broad aspects of computational biology and bioinformatics, including the development of theoretical models, empirical analysis of large scale datasets, method and software development, and establishment of unique databases that accelerate biological and biomedical discovery. We use computational, mathematical, and statistical techniques to address important questions in basic biomedicine and evolution. Our research ranges from theoretical modeling and simulation to tool development, with data scaling from the analysis of a single critical gene within a population to full genome comparisons across species as well as the analysis of individual images capturing functional and expression domains to thousands of images describing genome-wide expression patterns.
Discovery Bioinformatics Team
Core: Yuseob Kim, Sudhir Kumar, Michael S. Rosenberg, Jieping Ye
Key: Valentin Dinu, Philip Hedrick, Seungchan Kim, Ying-Cheng Lai, Banu Ozkan, Matthew Scotch, Jesse Taylor, Brian Verrelli
Senior: Corey J. Anderson, Fabia Battistuzzi, Alan Filipski, Jun Liu
Junior: Crystal Hepp, Jainhui Chen, Liang Sun, Lei Yuan, Ji Liu, Zheng Zhao
Koichiro Tamura, Tokyo Metropolitan U
S. Blair Hedges, Penn State U
Masatoshi Nei, Penn State U