Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection

Yoshinori Tamada, Sun Yong Kim, Hideo Bannai, Seiya Imoto, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano

Research output: Contribution to journalArticlepeer-review

140 Citations (Scopus)

Abstract

We present a statistical method for estimating gene networks and detecting promoter elements simultaneously. When estimating a network from gene expression data alone, a common problem is that the number of microarrays is limited compared to the number of variables in the network model, making accurate estimation a difficult task. Our method overcomes this problem by integrating the microarray gene expression data and the DNA sequence information into a Bayesian network model. The basic idea of our method is that, if a parent gene is a transcription factor, its children may share a consensus motif in their promoter regions of the DNA sequences. Our method detects consensus motifs based on the structure of the estimated network, then re-estimates the network using the result of the motif detection. We continue this iteration until the network becomes stable. To show the effectiveness of our method, we conducted Monte Carlo simulations and applied our method to Saccharomyces cerevisiae data as a real application.

Original languageEnglish
Pages (from-to)ii227-ii236
JournalBioinformatics
Volume19
Issue numberSUPPL. 2
DOIs
Publication statusPublished - 2003

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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