New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data

Background: Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential. Results: Here we describe a Bayesian network approach that addresses a specific network within a large dataset to discover new components. Our algorithm draws individual genes from a large gene-expression repository, and ranks them as potential members of a known pathway. We apply this method to discover new components of the cAMP-dependent protein kinase (PKA...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Anup Parikh, Eryong Huang, Christopher Dinh, Blaz Zupan, Adam Kuspa, Devika Subramanian, Gad Shaulsky
• Keywords: Bayesian Network, BMCBI 2010, Genetic Networks, PKA Activity