Biclustering Gene Expression Data Based on a High Dimensional Geometric Method

  • XIANG-CHAO GAN, ALAN WEE-CHUNG, Hong Yan
  • Published 2009

Abstract

In gene expression data, a bicluster is a subset of genes exhibiting a consistent pattern over a subset of the conditions. In this paper, we propose a new method to detect biclusters in gene expression data. Our approach is based on the high dimensional geometric property of biclusters and it avoids dependence on specific patterns, which degrade many available biclustering algorithms. Furthermore, we illustrate that a bilclustering algorithm can be decomposed into two independent steps and this not only helps to build up a hierarchical structure but also provides a coarse-to-fine mechanism and overcome the effect of the inherent noise in gene expression data. The simulated experiments demonstrate that our algorithm is very promising.

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Cite this paper

@inproceedings{GAN2009BiclusteringGE, title={Biclustering Gene Expression Data Based on a High Dimensional Geometric Method}, author={XIANG-CHAO GAN and ALAN WEE-CHUNG and Hong Yan}, year={2009} }