Predicting prognostic markers for glioma using gene co-expression network analysis

2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010

Volumn , Issue , 2010, Pages 546-551

  Uppalapati, Praneeth ^a   Xiang, Yang ^b   Huang, Kun ^b  

^a Ohio State University   (United States)

^b OHIO STATE UNIVERSITY   (United States)

Author keywords

Biomarker; Gene co expression network; Glioblastoma; Quasi clique; Tumor microenvironment

Indexed keywords

BIOLOGICAL INSIGHT; BIOLOGICAL PROCESS; CO-EXPRESSION NETWORKS; FINDING ALGORITHM; GENE CLUSTERS; GLIOBLASTOMAS; QUASI-CLIQUE; SURVIVAL TIME; TUMOR MICROENVIRONMENT;

BIOINFORMATICS; BIOMARKERS; ELECTRIC NETWORK ANALYSIS; TUMORS;

GENE EXPRESSION;

EID: 77958070004     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1854776.1854879     Document Type: Conference Paper

References (14)

1. Farmer, P., et al., A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer. Nat Med, 2009. 15(1): p. 68-74.
2. Zhang, J., et al., Using Frequent Co-expression Network to Identify Gene Clusters for Breast Cancer Prognosis, in International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS). 2009, IEEE Computer Society: Shanghai.
3. Zhang, J., et al. Predicting biomarkers for chronic lymphocytic leukemia using gene co-expression network analyses for ZAP70. in AMIA Summit of Translational Bioinformatics. 2010. San Francisco, CA.
4. Hu, H., et al., Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics, 2005. 21 Suppl 1: p. i213-21.
5. Zhang, B. and S. Horvath, A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol, 2005. 4: p. Article17.
6. Pujana, M.A., et al., Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet, 2007. 39(11): p. 1338-49.
7. Langfelder, P. and S. Horvath, Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol, 2007. 1: p. 54.
8. Langfelder, P. and S. Horvath, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008. 9: p. 559.
9. Li, A. and S. Horvath, Network module detection: Affinity search technique with the multi-node topological overlap measure. BMC Res Notes, 2009. 2: p. 142.
10. MacLennan, N.K., et al., Weighted gene co-expression network analysis identifies biomarkers in glycerol kinase deficient mice. Mol Genet Metab, 2009. 98(1-2): p. 203-14.
11. Yip, A.M. and S. Horvath, Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics, 2007. 8: p. 22.
12. Bredel, M., et al., A network model of a cooperative genetic landscape in brain tumors. Jama, 2009. 302(3): p. 261-75.
13. Schwartzbaum, J.A., et al., An international casecontrol study of interleukin-4Ralpha, interleukin-13, and cyclooxygenase-2 polymorphisms and glioblastoma risk. Cancer Epidemiol Biomarkers Prev, 2007. 16(11): p. 2448-54.
14. Schwartzbaum, J.A., et al., Allergy and inflammatory transcriptome is predominantly negatively correlated with CD133 expression in glioblastoma. Neuro Oncol. 12(4): p. 320-7.