Ensemble inference by integrative cancer networks

Frontiers in Genetics

Volumn 5, Issue MAR, 2014, Pages

  Mora, Antonio ^a,b   Taranta, Monia ^c   Zaki, Nazar ^b   Badidi, Elarbi ^b   Cinti, Caterina ^c   Capobianco, Enrico ^a,d  

^a INSTITUTE OF CLINICAL PHYSIOLOGY   (Italy)

^b UNITED ARAB EMIRATES UNIVERSITY   (United Arab Emirates)

^c CNR   (Italy)

^d UNIVERSITY OF MIAMI   (United States)

Author keywords

Cancer markers; Epigenetic therapy; Modularity; Networks; Predictive inference

Indexed keywords

CASPASE 9; EPIDERMAL GROWTH FACTOR; MICROTUBULE ASSOCIATED PROTEIN 2; MITOGEN ACTIVATED PROTEIN KINASE 14; PHOSPHATIDYLINOSITOL 3 KINASE; PLATELET DERIVED GROWTH FACTOR BETA RECEPTOR; STAT5A PROTEIN; TUMOR MARKER;

ARTICLE; BIOINFORMATICS; DNA MICROARRAY; DOWN REGULATION; GENE; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; MICROARRAY ANALYSIS; NEOPLASM; PROTEIN EXPRESSION; PROTEIN LOCALIZATION; REPRODUCIBILITY; SIGNAL TRANSDUCTION; UPREGULATION;

EID: 84897978756     PISSN: None     EISSN: 16648021     Source Type: Journal    
DOI: 10.3389/fgene.2014.00059     Document Type: Article

References (13)

1. Bebek, G., Koyutürk, M., Price, N. D., and Chance, M. R. (2012). Network biology methods integrating biological data for translational science. Brief. Bioinform. 13, 446-459. doi: 10.1093/bib/bbr075
2. Ben-Hamo, R., and Efroni, S. (2013). Networks as biomarkers. Syst. Biomed. 1, 34-41. doi: 10.4161/sysb.26474
3. Bock, C. (2012). Analysing and interpreting DNA methylated data. Nat. Rev. Genet. 13, 705-719. doi: 10.1038/nrg3273
4. Chuang, H.-Y., Lee, E., Liu, Y.-T., Lee, D., and Ideker, T. (2007). Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140. doi: 10.1038/msb4100180
5. Dao, P., Wang, K., Collins, C., Ester, M., Lapuk, A., and Sahinal, S. C. (2011). Optimally discriminative subnetwork markers predict response to chemotherapy. Bioinformatics 27, 1205-1213. doi: 10.1093/bioinformatics/btr245
6. Ein-Dor, L., Kela, I., Getz, G., Givol, D., and Domany, E. (2005). Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21, 171-178. doi: 10.1093/bioinformatics/bth469
7. Ein-Dor, L., Zuk, O., and Domany, E. (2006). Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl. Acad. Sci. U.S.A. 103, 5923-5928. doi: 10.1073/pnas.0601231103
8. Esteller, M. (2007). Cancer epigenomics: DNA methylomes and histone-modification maps. Nat. Rev. Genet. 8, 286-298. doi: 10.1038/nrg2005
9. Lee, E., Chuang, H.-Y., Kim, J.-W., and Ideker, T. (2008). Inferring pathway activity toward precise disease classification. PLoS Comput. Biol. 4:e1000217. doi: 10.1371/journal.pcbi.1000217
10. Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C., and Morris, Q. (2008). GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9(Suppl. 1), S4. doi: 10.1186/gb-2008-9-s1-s4
11. Peer, D., and Hacohen, N. (2011). Principles and strategies for developing network models in cancer. Cell 144, 864-873. doi: 10.1016/j.cell.2011.03.001
12. Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., and Chao, P. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38(Suppl. 2), W214-W220. doi: 10.1093/nar/gkq537
13. Wu, X., Huang, H., Wei, T., Pandey, R., Reinhard, C., and Li, S. D. (2012). Network expansion and pathway enrichment analysis towards biologically significant findings from microarrays. J. Integr. Bioinform. 9, 213. doi: 10.2390/biecoll-b-2012-213