Inferring Cellular Networks Using Probabilistic Graphical Models

Nir Friedman*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

933 Scopus citations

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.

Original languageAmerican English
Pages (from-to)799-805
Number of pages7
JournalScience
Volume303
Issue number5659
DOIs
StatePublished - 6 Feb 2004

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