Using Bayesian networks to analyze expression data

Nir Friedman*, Michal Linial, Iftach Nachman, Dana Pe'er

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

161 Scopus citations

Abstract

DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a `snapshot' of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multi-variate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes, and for providing clear methodologies for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes. We then present an efficient algorithm capable of learning such networks and a statistical method to assess our confidence in their features. Finally, we apply this method to the S. cerevisiae cell-cycle measurements of Spellman et al. to uncover biological features.

Original languageAmerican English
Pages127-135
Number of pages9
DOIs
StatePublished - 2000
EventRECOMB 2000: 4th Annual International Conference on Computational Molecular Biology - Tokyo, Jpn
Duration: 8 Apr 200011 Apr 2000

Conference

ConferenceRECOMB 2000: 4th Annual International Conference on Computational Molecular Biology
CityTokyo, Jpn
Period8/04/0011/04/00

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