International Journal of Computer Mathematics - Bioinformatics
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A new test system for stability measurement of marker gene selection in DNA microarray data analysis
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Fuzzy classifier based feature reduction for better gene selection
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
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Constructing a classifier based on microarray gene expression data has recently emerged as an important problem for cancer classification. Recent results have suggested the feasibility of constructing such a classifier with reasonable predictive accuracy under the circumstance where only a small number of cancer tissue samples of known type are available. Difficulty arises from the fact that each sample contains the expression data of a vast number of genes and these genes may interact with one another. Selection of a small number of critical genes is fundamental to correctly analyze the otherwise overwhelming data. It is essential to use a multivariate approach for capturing the correlated structure in the data. However, the curse of dimensionality leads to the concern about the reliability of selected genes. Here, we present a new gene selection method in which error and repeatability of selected genes are assessed within the context of M-fold cross-validation. In particular, we show that the method is able to identify source variables underlying data generation.