New components of the <Emphasis Type="Italic">Dictyostelium</Emphasis> PKA pathway revealed by Bayesian analysis of expression data |
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| Authors: | Anup Parikh Eryong Huang Christopher Dinh Blaz Zupan Adam Kuspa Devika Subramanian Gad Shaulsky |
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| Institution: | (1) Graduate program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA;(2) Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA;(3) Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA;(4) Faculty of Computer and Information Science, University of Ljubljana, Trzaska cesta 25, SI-1001 Ljubljana, Slovenia;(5) Department of Computer Science, Rice University, 6100 Main St, MS 132, Houston, TX 77005, USA |
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| Abstract: | 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. |
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