Analyzing gene expression profiles with ICA

D. Lutter, K. Stadlthanner, F. Theis, E. W. Lang, A. M. Tomé, B. Becker, Th Vogt

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

3 Scopus citations


High-throughput genome-wide measurements of gene transcript levels have become available with the recent development of microarray technology. Intelligent and efficient mathematical and computational analysis tools are needed to read and interpret the information content buried in those large scale gene expression patterns at various levels of resolution. But the development of such methods is still in its infancy. Modern machine learning and data mining techniques based on information theory, like independent component analysis (ICA), consider gene expression patterns as a superposition of independent expression modes which are considered putative independent biological processes. We focus on two widely used ICA algorithms to blindly decompose gene expression profiles into independent component profiles representing underlying biological processes. These exploratory methods will be capable of detecting similarity, locally or globally, in gene expression patterns and help to group genes into functional categories - for example, genes that are expressed to a greater or lesser extent in response to a drug or an existing disease.

Original languageEnglish
Title of host publicationProceedings of the Fourth IASTED International Conference on Biomedical Engineering
Number of pages6
StatePublished - 2006
Externally publishedYes
Event4th IASTED International Conference on Biomedical Engineering - Innsbruck, Austria
Duration: 15 Feb 200617 Feb 2006

Publication series

NameProceedings of the Fourth IASTED International Conference on Biomedical Engineering


Conference4th IASTED International Conference on Biomedical Engineering


  • FastICA
  • Gene expression profiles
  • Independent component analysis
  • JADE
  • Microarrays


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