Inferring cellular networks using probabilistic graphical models.

by: Nir Friedman

Science, Vol. 303, No. 5659. (6 February 2004), pp. 799-805.

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摘要

High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data.

@article{Friedman2004, abstract = {High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data.}, address = {School of Computer Science and Engineering, Hebrew University, 91904 Jerusalem, Israel. nir@cs.huji.ac.il}, author = {Friedman, Nir }, citeulike-article-id = {100166}, doi = {http://dx.doi.org/10.1126/science.1094068}, issn = {1095-9203}, journal = {Science}, keywords = {bayes, dt, inference, review}, month = {February}, number = {5659}, pages = {799--805}, posted-at = {2005-12-29 16:57:37}, priority = {0}, title = {Inferring cellular networks using probabilistic graphical models.}, url = {http://dx.doi.org/10.1126/science.1094068}, volume = {303}, year = {2004} }

RIS record

TY - JOUR ID - Friedman2004 L3 - citeulike-article-id:100166 N2 - High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data. IS - 5659 JF - Science SN - 1095-9203 AD - School of Computer Science and Engineering, Hebrew University, 91904 Jerusalem, Israel. nir@cs.huji.ac.il EP - 805 TI - Inferring cellular networks using probabilistic graphical models. VL - 303 SP - 799 KW - bayes KW - dt KW - inference KW - review AU - Friedman, Nir PY - 2004/02/06/ UR - http://dx.doi.org/10.1126/science.1094068 ER -

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