Andrey Rzhetsky Lab
IGSB Core Faculty
Faculty and Senior Fellow, CI
Professor, Dept. of Medicine, Section of Genetic Medicine, The University of Chicago
The University of Chicago
900 East 57th Street
KCBD
Chicago, IL
Phone: (773) 834-7367 Fax: (773) 834-2877
About the Lab
My main interest is in (asymptotic) understanding how phenotypes, such as human healthy diversity and maladies, are implemented at the level of genes and networks of interacting molecules. To harvest as much information about known molecular interactions as possible, my group runs a large-scale text-mining effort aiming at analysis of a vast corpus of biomedical publications. Currently we can extract from text automatically about 500 distinct flavors of relations among biomedical entities (such as bind, activate, merystilate, and transport). To sharpen our text-mining axes, we are actively designing related models and computational applications. Furthermore, in cooperation with our experimentally talented colleagues, we are striving to use text-mined networks to understand, interpret and refine high- or low-throughput experimental data. We are also computationally generating biological hypotheses that our generous collaborators are attempting to test experimentally. My older (still smoldering) passion is in developing and applying computational methods related to phylogenetics and evolutionary biology.
Lab Members
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David Blair
PhD Student
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.(JavaScript must be enabled to view this email address) (773) 702-6799 |
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Andrey Rzhetsky
IGSB, Core Faculty
IGSB Core Faculty and CI Senior Fellow Professor, Dept. of Medicine, Section of Genetic Medicine, the University of Chicago |
.(JavaScript must be enabled to view this email address) (773) 834-7367 |
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Shi Yu
Postdoc
Postdoctoral Scholar |
.(JavaScript must be enabled to view this email address) (773) 702-6799 |
News
New data-mining effort launched to study mental disorders
The Sylvio O. Conte Center, a multi-institutional effort based at the University of Chicago, will combine the statistical power of pre-existing genetics, pharmacogenomics, text-mining and clinical record databases to confront diseases that have so far frustrated researchers.Press Release
