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Published in:
2006 | OriginalPaper | Chapter
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
Published in: Fuzzy Logic and Applications
Publisher: Springer Berlin Heidelberg
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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.