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Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models.

Yoshinori Tamada·Hideo Bannai·Seiya Imoto·Toshiaki Katayama·Minoru Kanehisa·Satoru Miyano
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Abstract

Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at http://bonsai.ims.u-tokyo.ac.jp/~tamada/bayesnet/.

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Bayes TheoremComputer SimulationEvolution, MolecularGene Expression ProfilingGene Expression RegulationGenes, cdcHumansModels, BiologicalModels, StatisticalProteomeSaccharomyces cerevisiaeSignal TransductionJournal Article

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Yoshinori Tamada, Hideo Bannai, Seiya Imoto, Toshiaki Katayama, Minoru Kanehisa, Satoru Miyano. 2005. Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models.. https://doi.org/10.1142/s0219720005001569

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