2000/08/01 by Nir Friedman, Michal Linial, Iftach Nachman +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian inference #Bayesian network #Bayesian probability #Biology #Computational biology #Computer science #Conditional independence #Data mining #Dynamic Bayesian network #Gene #Gene Regulatory Network Analysis #Gene expression #Gene expression and cancer classification #Gene regulatory network #Genetics #Machine learning #Snapshot (computer storage) #Systems biology #Variable-order Bayesian network
paper · doi:10.1089/106652700750050961
openalex publication_date 2000/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
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).