Symmetric and asymmetric multi-modality biclustering analysis for microarray data matrix

Sun Yuan Kung, Man Wai Mak, Ilias Tagkopoulos

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Machine learning techniques offer a viable approach to cluster discovery from microarray data, which involves identifying and classifying biologically relevant groups in genes and conditions. It has been recognized that genes (whether or not they belong to the same gene group) may be co-expressed via a variety of pathways. Therefore, they can be adequately described by a diversity of coherence models. In fact, it is known that a gene may participate in multiple pathways that may or may not be co-active under all conditions. It is therefore biologically meaningful to simultaneously divide genes into functional groups and conditions into co-active categories - leading to the so-called biclustering analysis. For this, we have proposed a comprehensive set of coherence models to cope with various plausible regulation processes. Furthermore, a multivariate biclustering analysis based on fusion of different coherence models appears to be promising because the expression level of genes from the same group may follow more than one coherence models. The simulation studies further confirm that the proposed framework enjoys the advantage of high prediction performance.

Original languageEnglish (US)
Pages (from-to)275-298
Number of pages24
JournalJournal of Bioinformatics and Computational Biology
Issue number2
StatePublished - Apr 2006

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Biochemistry
  • Computer Science Applications


  • Biclustering
  • Computational bioinformatics
  • Finite mixture models
  • Gene expression patterns
  • Machine learning
  • Microarray


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