Computational Biology and Chemistry
Computational Biology and Chemistry
A clustering coefficient for weighted networks, with application to gene expression data
AI Communications - Network Analysis in Natural Sciences and Engineering
International Journal of Computer Mathematics - Recent Advances in Computational and Applied Mathematics in Science and Engineering
An expert system to classify microarray gene expression data using gene selection by decision tree
Expert Systems with Applications: An International Journal
How to improve postgenomic knowledge discovery using imputation
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Comparative analysis of gene-coexpression networks across species
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Transcriptional gene regulatory network reconstruction through cross platform gene network fusion
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Artificial Intelligence in Medicine
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Motivation: Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer. Results: We collected 10 published gene expression datasets from cancers of 13 different tissues and constructed 2 distinct coexpression networks: a tumor network and normal network. Comparison of the two networks showed that cancer affected many coexpression relationships. Functional changes such as alteration in energy metabolism, promotion of cell growth and enhanced immune activity were accompanied with coexpression changes. Coregulation of collagen genes that may control invasion and metastatic spread of tumor cells was also found. Cluster analysis in the tumor network identified groups of highly interconnected genes related to ribosomal protein synthesis, the cell cycle and antigen presentation. Metallothionein expression was also found to be clustered, which may play a role in apoptosis control in tumor cells. Our results show that this model would serve as a novel method for analyzing microarrays beyond the specific implications for cancer. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: sskimb@ssu.ac.kr