Bayesian network analysis of signaling networks: a primer.

by: D Pe'er

Sci STKE, Vol. 2005, No. 281. (26 April 2005)

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摘要

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.

@article{p-bnasn-2005, 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.}, address = {Department of Genetics, Harvard Medical School, Boston, MA 02115, USA. dpeer@genetics.med.harvard.edu}, author = {Pe'er, D. }, citeulike-article-id = {470218}, doi = {http://dx.doi.org/10.1126/stke.2812005pl4}, issn = {1525-8882}, journal = {Sci STKE}, keywords = {actex, reverse\_engineering}, month = {April}, number = {281}, posted-at = {2008-04-17 17:08:41}, priority = {2}, title = {Bayesian network analysis of signaling networks: a primer.}, url = {http://dx.doi.org/10.1126/stke.2812005pl4}, volume = {2005}, year = {2005} }

RIS record

TY - JOUR ID - p-bnasn-2005 L3 - citeulike-article-id:470218 TI - Bayesian network analysis of signaling networks: a primer. JF - Sci STKE VL - 2005 IS - 281 AD - Department of Genetics, Harvard Medical School, Boston, MA 02115, USA. dpeer@genetics.med.harvard.edu SN - 1525-8882 N2 - 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. KW - actex KW - reverse_engineering AU - Pe'er, D PY - 2005/04/26/ UR - http://dx.doi.org/10.1126/stke.2812005pl4 ER -

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