Using Bayesian networks to analyze expression data

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Using Bayesian networks to analyze expression data (2000)

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by Nir Friedman , Michal Linial , Iftach Nachman

Venue:	Journal of Computational Biology
Citations:	1088 - 17 self

BibTeX

@ARTICLE{Friedman00usingbayesian,
    author = {Nir Friedman and Michal Linial and Iftach Nachman},
    title = {Using Bayesian networks to analyze expression data},
    journal = {Journal of Computational Biology},
    year = {2000},
    volume = {7},
    pages = {601--620}
}

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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 multivariate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes and because they provide a clear methodology for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes. We then describe a method for recovering gene interactions from microarray data using tools for learning Bayesian networks. Finally, we demonstrate this method on the S. cerevisiae cell-cycle measurements of Spellman et al. (1998). Key words: gene expression, microarrays, Bayesian methods. 1.

Keyphrases

bayesian network expression data gene protein interaction gene interaction cell-cycle measurement new framework microarray data joint multivariate probability distribution complex stochastic process statistical dependency gene expression conditional independence graph-based model computational biology cellular system expression level transcription level clear methodology multiple expression measurement major challenge dna hybridization bayesian method key biological feature key word

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