A joint framework for missing values estimation and biclusters detection in gene expression data

Kin On Cheng, Ngai Fong Law, Yui Lam Chan, Wan Chi Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


DNA microarray experiment unavoidably generates gene expression data with missing values. This hardens subsequent analysis such as biclusters detection which aims to find a set of co-expressed genes under some experimental conditions. Missing values are thus required to be estimated before biclusters detection. Existing missing values estimation algorithms rely on finding coherence among expression values throughout the data. In view that both missing values estimation and biclusters detection aim at exploiting coherence inside the expression data, we propose to integrate these two steps into a joint framework. The benefits are twofold; the missing values estimation can improve biclusters analysis and the coherence in detected biclusters can be exploited for accurate missing values estimation. Experimental results show that the bicluster information can significantly improve the accuracy in missing values estimation. Also, the joint framework enables the detection of biologically meaningful biclusters.
Original languageEnglish
Pages (from-to)574-586
Number of pages13
JournalInternational Journal of Bioinformatics Research and Applications
Issue number6
Publication statusPublished - 1 Jan 2014


  • Biclusters detection
  • Bioinformatics applications
  • Bioinformatics research
  • Gene expression data
  • Missing values estimation

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management
  • Biomedical Engineering
  • Clinical Biochemistry
  • Medicine(all)


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