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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
Web: http://qbmi.msu.edu
http://www.bch.msu.edu/GEDD/index.htm