Discriminatory Mining of Gene Expression Microarray Data

Zuyi Wang, Yue Wang, Jianping Lu, Sun Yuan Kung, Junying Zhang, Richard Lee, Jianhua Xuan, Javed Khan, Robert Clarke

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Recent advances in machine learning and pattern recognition methods provide new analytical tools to explore high dimensional gene expression microarray data. Our data mining software, VISual Data Analyzer for cluster discovery (VISDA), reveals many distinguishing patterns among gene expression profiles, which are responsible for the cell's phenotypes. The model-supported exploration of high-dimensional data space is achieved through two complementary schemes: dimensionality reduction by discriminatory data projection and cluster decomposition by soft data clustering. Reducing dimensionality generates the visualization of the complete data set at the top level. This data set is then partitioned into subclusters that can consequently be visualized at lower levels and if necessary partitioned again. In this paper, three different algorithms are evaluated in their abilities to reduce dimensionality and to visualize data sets: Principal Component Analysis (PCA), Discriminatory Component Analysis (DCA), and Projection Pursuit Method (PPM). The partitioning into subclusters uses the Expectation-Maximization (EM) algorithm and the hierarchical normal mixture model that is selected by the user and verified "optimally" by the Minimum Description Length (MDL) criterion. These approaches produce different visualizations that are compared against known phenotypes from the microarray experiments. Overall, these algorithms and user-selected models explore the high dimensional data where standard analyses may not be sufficient.

Original languageEnglish (US)
Pages (from-to)255-272
Number of pages18
JournalJournal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
Issue number3
StatePublished - Nov 2003
Externally publishedYes

Bibliographical note

Funding Information:
∗This work was supported in part by the National Institutes of Health under Grants 5R21CA83231. †Present address: Center for Genetic Research, Children’s National Medical Center, Washington, DC 20010, USA.

Copyright 2008 Elsevier B.V., All rights reserved.


  • Cluster visualization and selection
  • Computational bioinformatics
  • Finite normal mixture
  • Gene microarrays
  • Machine learning


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