Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Analysis of gene expression profiles: class discovery and leaf ordering
Proceedings of the sixth annual international conference on Computational biology
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficiently Mining Gene Expression Data via a Novel Parameterless Clustering Method
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene discovery in leukemia revisited: a computational intelligence perspective
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Reduct Generation and Classification of Gene Expression Data
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
Bioinformatics with soft computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary Rough Feature Selection in Gene Expression Data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The main challenge of gene selection from gene expression dataset is to reduce the redundant genes without affecting discernibility between objects. A pipelined approach combining feature ranking together with rough sets attribute reduction for gene selection is proposed. Feature ranking is used to narrow down the gene space as the first step, top ranked genes are selected; the minimal reduct is induced by rough sets to eliminate the redundant attributes. An exploration of this approach on Leukemia gene expression data is conducted and good results are obtained with no preprocessing to the data. The experiment results show that this approach is successful for selecting high discriminative genes for cancer classification task.