Bicluster analysis of genome-wide gene expression

Kuanchung Chen*, Yuh-Jyh Hu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

A number of biclustering approaches have been developed to mitigate the limitations of standard clustering algorithms. They have different problem formulation, search strategy and computational complexity. We proposed a new biclustering method based on the framework of market basket analysis in which a bicluster is described as a frequent itemset. As a feasibility test, we compared it with several standard clustering algorithms on a genome-wide yeast microarray dataset, and it showed very promising results. We later did a comparison between our approach and various current biclustering methods, following a systematic evaluation procedure recently published. The experimental results demonstrate that our new method outperforms the others.

Original languageEnglish
Title of host publicationProceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06
Pages225-231
Number of pages7
DOIs
StatePublished - 1 Dec 2006
Event3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB - Toronto, ON, Canada
Duration: 28 Sep 200629 Sep 2006

Publication series

NameProceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06

Conference

Conference3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB
CountryCanada
CityToronto, ON
Period28/09/0629/09/06

Keywords

  • Biclustering
  • Clustering
  • Expression
  • Microarray

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