Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Feature selection for classifying high-dimensional numerical data
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Gene Selection is one class of most used data analysis algorithms on microarray dataset. The goal of gene selection algorithms is to filter out a small set of informative genes that best explains experimental variations. Traditional gene selection algorithms are mostly single-gene based. Some discriminative scores are calculated and sorted for each gene. Top ranked genes are then selected as informative genes for further study. Such algorithms ignore completely correlations between genes, although such correlations is widely known. Genes interact with each other through various pathways and regulative networks. In this paper, we propose to use, instead of ignoring, such correlations for gene selection. Experiments performed on three public available datasets show promising results.