Gene regulatory network clustering for graph layout based on microarray gene expression data.

Kaname Kojima, Seiya Imoto, Masao Nagasaki, Satoru Miyano

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a statistical model realizing simultaneous estimation of gene regulatory network and gene module identification from time series gene expression data from microarray experiments. Under the assumption that genes in the same module are densely connected, the proposed method detects gene modules based on the variational Bayesian technique. The model can also incorporate existing biological prior knowledge such as protein subcellular localization. We apply the proposed model to the time series data from a synthetically generated network and verified the effectiveness of the proposed model. The proposed model is also applied the time series microarray data from HeLa cell. Detected gene module information gives the great help on drawing the estimated gene network.

Original languageEnglish
Pages (from-to)84-95
Number of pages12
JournalGenome informatics. International Conference on Genome Informatics
Volume24
Publication statusPublished - 2010 Jan 1

ASJC Scopus subject areas

  • Medicine(all)

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