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A New Framework for Co-clustering of Gene Expression Data

  • Shuzhong Zhang
  • Kun Wang
  • Bilian Chen
  • Xiuzhen Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)

Abstract

A new framework is proposed to study the co-clustering of gene expression data. This framework is based on a generic tensor optimization model and an optimization method termed Maximum Block Improvement (MBI) recently developed in [3]. Not only can this framework be applied for co-clustering gene expression data with genes expressed at different conditions represented in 2D matrices, but it can also be readily applied for co-clustering more complex high-dimensional gene expression data with genes expressed at different tissues, different development stages, different time points, different stimulations, etc. Moreover, the new framework is so flexible that it poses no difficulty at all to incorporate a variety of clustering quality measurements. In this paper, we demonstrate the effectiveness of this new approach by providing the details of one specific implementation of the algorithm, and presenting the experimental testing on microarray gene expression datasets. Our results show that the new algorithm is very efficient and it performs well for identifying patterns in gene expression datasets.

Keywords

Gene Expression Data Gene Expression Dataset Yeast Cell Cycle Assignment Matrix Yeast Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shuzhong Zhang
    • 1
  • Kun Wang
    • 2
  • Bilian Chen
    • 3
  • Xiuzhen Huang
    • 2
  1. 1.Industrial and Systems Engineering ProgramUniversity of MinnesotaMinneapolisUSA
  2. 2.Department of Computer ScienceArkansas State UniversityJonesboroUSA
  3. 3.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatinHong Kong

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