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
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge guided analysis of microarray data
Journal of Biomedical Informatics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Virtual gene: using correlations between genes to select informative genes on microarray datasets
Transactions on Computational Systems Biology II
An information theoretic approach to assessing gene-ontology-driven similarity and its application
International Journal of Data Mining and Bioinformatics
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Previous studies have proven that it is feasible to build sample classifiers using gene expression profiles. To build an effective sample classifier, dimension reduction process is necessary since classic pattern recognition algorithms do not work well in high dimensional space. In this paper, we present a novel feature extraction algorithm by integrating microarray expression data with Gene Ontology (GO). Applying semantic similarity measures, we identify the groups of genes, called virtual genes, which potentially interact with each other for a biological function. The correlation in expressions of virtual genes is used to classify samples. For colon cancer data, this approach significantly improved the classification accuracy by more than 10%.