Exploiting biclustering for missing value estimation in DNA microarray data

K. O. Cheng, Ngai Fong Law, W. C. Siu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The missing values in gene expression data harden subsequent analysis such as biclustering which aims to find a set of coexpressed genes across a number of experimental conditions. Missing values are thus required to be estimated before biclusters detection. Existing estimation algorithms rely on finding coherence among expression values throughout the entire genes and/or across all the conditions. In view that both missing values estimation and biclusters detection aim at exploiting coherence inside the expression data, we propose to integrate them into a single framework. The benefits are twofold, the missing value estimation can improve bicluster analysis and the coherence in detected biclusters can be exploited for better missing value estimation. Experimental results show that the integrated framework outperforms existing missing values estimation algorithms. It reduces error in missing value estimation and facilitates the detection of biologically meaningful biclusters.
Original languageEnglish
Title of host publication2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
Publication statusPublished - 1 Dec 2012
Event2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012 - Hollywood, CA, United States
Duration: 3 Dec 20126 Dec 2012

Conference

Conference2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
Country/TerritoryUnited States
CityHollywood, CA
Period3/12/126/12/12

Keywords

  • biclustering
  • Gene expression
  • missing value estimation

ASJC Scopus subject areas

  • Information Systems

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