Bayesian Network Analysis of Signaling Networks: A Primer
- 26 April 2005
- journal article
- review article
- Published by American Association for the Advancement of Science (AAAS) in Science's STKE
- Vol. 2005 (281) , pl4-4
- https://doi.org/10.1126/stke.2812005pl4
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
This publication has 15 references indexed in Scilit:
- Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell DataScience, 2005
- Inferring quantitative models of regulatory networks from expression dataBioinformatics, 2004
- Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression dataBiosystems, 2004
- Using Bayesian Networks to Analyze Expression DataJournal of Computational Biology, 2000
- Learning Bayesian Networks with Local StructurePublished by Springer Nature ,1998
- Learning Bayesian Networks is NP-CompletePublished by Springer Nature ,1996
- Learning Bayesian Networks: The Combination of Knowledge and Statistical DataPublished by Elsevier ,1994
- Learning Gaussian NetworksPublished by Elsevier ,1994
- d-Separation: From Theorems to AlgorithmsPublished by Elsevier ,1990
- BAYESIAN INFERENCEPublished by Elsevier ,1988