Authors: Pe'er D
Abstract: High-throughput proteomic data can be used to reveal the connectivity of signaling networks and the influences between signaling molecules. We present a primer on the use of Bayesian networks for this task. Bayesian networks have been successfully used to derive causal influences among biological signaling molecules (for example, in the analysis of intracellular multicolor flow cytometry). We discuss ways to automatically derive a Bayesian network model from proteomic data and to interpret the resulting model.
Keywords: Algorithms; Animals; *Bayes Theorem; Causality; Genotype; Humans; Likelihood Functions; Markov Chains; Models, Biological; *Proteomics; *Signal Transduction
Journal: Science's STKE : signal transduction knowledge environment Volume: 2005 Issue: 281 Pages: pl4 Date: April 28, 2005 PMID: 15855409 |
Pe'er D (2005) Bayesian network analysis of signaling networks: a primer. Science's STKE : signal transduction knowledge environment 2005: pl4.
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