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

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1295-1313
Number of pages19
JournalJournal of bioinformatics and computational biology
Issue number6
Publication statusPublished - Dec 2005

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications


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