Nucleic Acids Research Advance Access originally published online on August 16, 2006
Nucleic Acids Research 2006 34(14):4069-4080; doi:10.1093/nar/gkl583
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Nucleic Acids Research, 2006, Vol. 34, No. 14 4069-4080
© 2006 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Topology-based cancer classification and related pathway mining using microarray data
1 Department of Computer Science, National Chung-Hsing University Taichung, Taiwan, ROC 2 Institutes of Biomedical Sciences and Molecular Biology, National Chung-Hsing University Taichung, Taiwan, ROC 3 Department of Biotechnology and Bioinformatics, Asia University Taichung, Taiwan, ROC 4 Graduate Institute of Epidemiology, National Taiwan University Taipei, Taiwan, ROC 5 NTU Center for Genomic Medicine, National Taiwan University College of Medicine Taipei, Taiwan, ROC
*To whom correspondence should be addressed. Tel: 886 4 22840485; ext. 226; Fax: 886 4 22853469; Email: jwchen{at}dragon.nchu.edu.tw
Received May 29, 2006. Revised July 24, 2006. Accepted July 26, 2006.
Cancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.
*Correspondence may also be addressed to P.C. Chang. Tel: 886 4 23323456; ext. 1868; Fax: 886 4 23316699; Email: pcchang{at}asia.edu.tw
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