Inferring cellular networks using probabilistic graphical models.

http://www.ncbi.nlm.nih.gov/pubmed/14764868


1. Science. 2004 Feb 6;303(5659):799-805.

Inferring cellular networks using probabilistic graphical models.

Friedman N.

School of Computer Science and Engineering, Hebrew University, 91904 Jerusalem,
Israel. nir@cs.huji.ac.il

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.


PMID: 14764868 [PubMed - indexed for MEDLINE]