Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Identification of Co-regulated Signature Genes in Pancreas Cancer- A Data Mining Approach
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Feature Selection Based on the Rough Set Theory and Expectation-Maximization Clustering Algorithm
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Computational Biology and Chemistry
A Biologically Inspired Measure for Coexpression Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Active mining discriminative gene sets
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Motivation: To understand cancer etiology, it is important to explore molecular changes in cellular processes from normal state to cancerous state. Because genes interact with each other during cellular processes, carcinogenesis related genes may form differential co-expression patterns with other genes in different cell states. In this study, we develop a statistical method for identifying differential gene--gene co-expression patterns in different cell states. Results: For efficient pattern recognition, we extend the traditional F-statistic and obtain an Expected Conditional F-statistic (ECF-statistic), which incorporates statistical information of location and correlation. We also propose a statistical method for data transformation. Our approach is applied to a microarray gene expression dataset for prostate cancer study. For a gene of interest, our method can select other genes that have differential gene--gene co-expression patterns with this gene in different cell states. The 10 most frequently selected genes, include hepsin, GSTP1 and AMACR, which have recently been proposed to be associated with prostate carcinogenesis. However, genes GSTP1 and AMACR cannot be identified by studying differential gene expression alone. By using tumor suppressor genes TP53, PTEN and RB1, we identify seven genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene--gene expression patterns with many other genes in different cell states. By discovering such patterns, we may be able to identify carcinogenesis related genes. Availability: The R-codes for our study are available at http://bioinformatics.med.yale.edu/microarray/BioSupp1.html