Proceedings of the 2007 ACM symposium on Applied computing
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Computers in Biology and Medicine
Wrapper filtering criteria via linear neuron and kernel approaches
Computers in Biology and Medicine
A hybrid GA & back propagation approach for gene selection and classification of microarray data
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Ensemble Neural Networks with Novel Gene-Subsets for Multiclass Cancer Classification
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PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
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Computational Statistics & Data Analysis
New gene selection method for multiclass tumor classification by class centroid
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An expert system to classify microarray gene expression data using gene selection by decision tree
Expert Systems with Applications: An International Journal
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Pattern Recognition
Information Sciences: an International Journal
Semantic similarity based feature extraction from microarray expression data
International Journal of Data Mining and Bioinformatics
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RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
International Journal of Bioinformatics Research and Applications
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
SoFoCles: Feature filtering for microarray classification based on Gene Ontology
Journal of Biomedical Informatics
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PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Dimension reduction using semi-supervised locally linear embedding for plant leaf classification
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
A novel hybrid feature selection method for microarray data analysis
Applied Soft Computing
Using Gene Ontology to enhance effectiveness of similarity measures for microarray data
International Journal of Data Mining and Bioinformatics
A hybrid model to favor the selection of high quality features in high dimensional domains
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
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PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
A hybrid GA/SVM approach for gene selection and classification of microarray data
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Virtual gene: using correlations between genes to select informative genes on microarray datasets
Transactions on Computational Systems Biology II
Boost feature subset selection: a new gene selection algorithm for microarray dataset
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Active mining discriminative gene sets
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Engineering Applications of Artificial Intelligence
Toward an efficient and scalable feature selection approach for internet traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Motivation: Recent studies have shown that microarray gene expression data are useful for phenotype classification of many diseases. A major problem in this classification is that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. Many approaches have been proposed for this gene selection problem. Most of the previous gene ranking methods typically select 50--200 top-ranked genes and these genes are often highly correlated. Our goal is to select a small set of non-redundant marker genes that are most relevant for the classification task. Results: To achieve this goal, we developed a novel hybrid approach that combines gene ranking and clustering analysis. In this approach, we first applied feature filtering algorithms to select a set of top-ranked genes, and then applied hierarchical clustering on these genes to generate a dendrogram. Finally, the dendrogram was analyzed by a sweep-line algorithm and marker genes are selected by collapsing dense clusters. Empirical study using three public datasets shows that our approach is capable of selecting relatively few marker genes while offering the same or better leave-one-out cross-validation accuracy compared with approaches that use top-ranked genes directly for classification. Availability: The HykGene software is freely available at http://www.cs.dartmouth.edu/~wyh/software.htm Contact: wyh@cs.dartmouth.edu Supplementary information: Supplementary material is available from http://www.cs.dartmouth.edu/~wyh/hykgene/supplement/index.htm