Using Bayesian networks to analyze expression data.

@article{citeulike:1021, 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).}, address = {School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel. nir@cs.huji.ac.il}, author = {Friedman, N. and Linial, M. and Nachman, I. and Pe'er, D. }, citeulike-article-id = {1021}, doi = {10.1089/106652700750050961}, issn = {1066-5277}, journal = {J Comput Biol}, keywords = {bayesian\_network, genetic\_regulatory\_network, microarray\_inference}, number = {3-4}, pages = {601--620}, posted-at = {2008-01-11 13:48:50}, priority = {4}, title = {Using Bayesian networks to analyze expression data.}, url = {http://dx.doi.org/10.1089/106652700750050961}, volume = {7}, year = {2000} }

TY - JOUR ID - citeulike:1021 L3 - citeulike-article-id:1021 TI - Using Bayesian networks to analyze expression data. JF - J Comput Biol VL - 7 IS - 3-4 SP - 601 EP - 620 AD - School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel. nir@cs.huji.ac.il SN - 1066-5277 N2 - 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). KW - bayesian_network KW - genetic_regulatory_network KW - microarray_inference AU - Friedman, N AU - Linial, M AU - Nachman, I AU - Pe'er, D PY - 2000/// UR - http://dx.doi.org/10.1089/106652700750050961 ER -