Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis

Authors:
Leif E. Peterson;Matthew A. Coleman
Affiliations:
Baylor College of Medicine, Houston, TX;Lawrence Livermore National Laboratory, Livermore, CA
Venue:
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Year:
2005

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Abstract

A combination of gene ranking, dimensional reduction, and recursive feature elimination (RFE) using a BP-MLP artificial neural network (ANN) was used to select genes for DNA microarray classification. Use of k-means cluster analysis for dimensional reduction and maximum sensitivity for RFE resulted in 64-gene models with fewer invariant and correlated features when compared with PCA and mimimum error. In conclusion, k-means cluster analysis and sensitivity may be better suited for classifying diseases for which gene expression is more strongly influenced by pathway heterogeneity.