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SCIENCE AT THE EDGE SEMINAR QB/GEDD Friday, September 10 at 11:30am Room 1400 Biomedical and Physical Sciences Bldg. Refreshments at 11:15 Jun S Liu Department of Statistics, Harvard University All about Interactions In this talk we will report some of our recent efforts in developing methods for detecting various interactions, such as protein-protein interactions, gene-gene interactions, and amino-acid mutation interactions. We will start with a simple Bayesian network method for mining protein-protein interactions from unstructured text (such as pubmed abstracts). The method not only extracts the names of the interacting proteins, but also the verb describing how they interact. The method achieved an overall accuracy of 87% on a cross-validation test, and can be used to complement human annotations to extract a large number of new PPIs from literature. The webserver can be found at http://www.biotextminer.com/TextMining3/index.html. Next, we consider the problem of detecting gene-gene interactions that may affect a certain trait, such as the risk of type-1 diabetes, or the expressions of a large set of genes. Because of the large number of genes and genetic markers in such studies, it is extremely challenging to discover how a small number of genetic markers interact with each other to affect certain human traits. We propose a Bayesian partition model to tackle the problem. Our extensive simulation studies mimicked such data structures and demonstrated that our Bayesian approach is much more powerful than the standard two-stage step-wise approach as practiced by statisticians and geneticists. Helen Geiger Administrative Assistant Quantitative Biology Graduate Program/ Gene Expression in Development & Disease Michigan State University 502B Biochemistry Building East Lansing, MI 48824 Phone: (517) 432-9895 Fax: (517) 353-9334 E-mail: [log in to unmask]<mailto:[log in to unmask]> Web: http://qbmi.msu.edu<http://qbmi.msu.edu/> http://www.bch.msu.edu/GEDD/index.htm<http://www.bch.msu.edu/GEDD/MSU-TRSB-Symposium.htm>