Residual bootstrapping and median filtering for robust estimation of gene networks from microarray data

  • Authors:

  • Seiya Imoto;Tomoyuki Higuchi;SunYong Kim;Euna Jeong;Satoru Miyano

  • Affiliations:

  • Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan;Institute of Statistical Mathematics, Tokyo, Japan;Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan;Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan;Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan

  • Venue:
  • CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
  • Year:
  • 2004

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Abstract

We propose a robust estimation method of gene networks based on microarray gene expression data. It is well-known that microarray data contain a large amount of noise and some outliers that interrupt the estimation of accurate gene networks. In addition, some relationships between genes are nonlinear, and linear models thus are not enough for capturing such a complex structure. In this paper, we utilize the moving boxcel median filter and the residual bootstrap for constructing a Bayesian network in order to attain robust estimation of gene networks. We conduct Monte Carlo simulations to examine the properties of the proposed method. We also analyze Saccharomyces cerevisiae cell cycle data as a real data example.