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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shengjun Hong Hua Dong Li Jin Momiao Xiong |
| Copyright Year | 2010 |
| Description | Author affiliation: Human Genetics Center, University of Texas School of Public Health, Houston, 77030, USA (Momiao Xiong) || State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200433, China (Shengjun Hong; Hua Dong; Li Jin) |
| Abstract | Ovarian cancer is one of the leading causes of death in women. To describe the complex gene regulatory relationships and investigate genes acting important roles in ovarian cancer, we adopted gaussian graphic model to construct gene co-expression networks of two independent ovarian cancer datasets separately. To validate the robustness of networks, modules are identified by decision tree cut algorithm and their functions were investigated. Our results showed that the inferred networks were structurally conservative and the identified modules were highly overlapped across the datasets. We discovered four conserved modules which were enriched with the genes in four cancer related pathways. Besides, we detected an ovarian cancer related gene CCEN2 and other six cancer related genes which may also play important roles in ovarian cancer. All the above results showed that incorporating gene co-expression network into the gene expression analysis may facilitate the discovery of cancer mechanisms. |
| Starting Page | 269 |
| Ending Page | 274 |
| File Size | 484737 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424483037 |
| e-ISBN | 9781424483044 |
| DOI | 10.1109/BIBMW.2010.5703811 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Correlation DNA Genomics Robustness Bioinformatics Cancer Tumors |
| Content Type | Text |
| Resource Type | Article |
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