SOM-based class discovery exploring the ICA-reduced features of microarray expression profiles

Andrei Dragomir, Seferina Mavroudi, Anastasios Bezerianos

Research output: Contribution to journalArticlepeer-review


Gene expression datasets are large and complex, having many variables and unknown internal structure. We apply independent component analysis (ICA) to derive a less redundant representation of the expression data. The decomposition produces components with minimal statistical dependence and reveals biologically relevant information. Consequently, to the transformed data, we apply cluster analysis (an important and popular analysis tool for obtaining an initial understanding of the data, usually employed for class discovery). The proposed self-organizing map (SOM)-based clustering algorithm automatically determines the number of 'natural' subgroups of the data, being aided at this task by the available prior knowledge of the functional categories of genes. An entropy criterion allows each gene to be assigned to multiple classes, which is closer to the biological representation. These features, however, are not achieved at the cost of the simplicity of the algorithm, since the map grows on a simple grid structure and the learning algorithm remains equal to Kohonen's one.

Original languageEnglish (US)
Pages (from-to)596-616
Number of pages21
JournalComparative and Functional Genomics
Issue number8
StatePublished - Dec 2004
Externally publishedYes


  • Class discovery
  • Clustering
  • Independent component analysis
  • Microarrays
  • Self-organizing maps

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Biology
  • Genetics


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