Inferring transcription networks in plant stress response
Date: 14 May 2008 (Wednesday)
Time:12:30 pm – 1:30 pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Prof. GUO Dianjing, Assistant Professor, Department of Biology, CUHK
Abstract: Bayesian Networks have been praised for being a powerful tool for performing probabilistic inference of molecular networks. However, they do have limitations that impede their application to complex biological systems, mainly because Bayesian Networks assume a simple attribute-value representation. Here we propose a novel probabilistic graphical model framework which integrates information from different biological data sources for gene function inference and regulatory network discovery. The approach is based on the assumption that genes involved in the same cellular process tend to have similar expression pattern and regulated by the same sets of regulatory proteins at transcription level. By applying our algorithm to publicly available gene expression profiling dataset on Arabidopsis thaliana, we provide functional predictions for stress-relevant genes and infer transcription network underlying plant stress response.
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