Modeling disease progression using dynamics of pathway connectivity

Bioinformatics

Volumn 30, Issue 16, 2014, Pages 2343-2350

  Ma, Xiaoke ^a   Gao, Long ^b   Tan, Kai ^a  

^a UNIVERSITY OF IOWA   (United States)

^b University of Iowa   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOLOGICAL MODEL; BREAST TUMOR; CLASSIFICATION; DISEASE COURSE; FEMALE; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETICS; HUMAN; SUPPORT VECTOR MACHINE;

ALGORITHMS; BREAST NEOPLASMS; DISEASE PROGRESSION; FEMALE; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; HUMANS; MODELS, GENETIC; SUPPORT VECTOR MACHINE;

EID: 84906256025     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu298     Document Type: Article

References (42)

1. Andre, F. et al. (2013) Molecular pathways: involvement of immune pathways in the therapeutic response and outcome in breast cancer. Clin. Cancer Res., 19, 28-33.
2. Ashburner, M. et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet., 25, 25-29.
3. Bandyopadhyay, S. et al. (2010) Rewiring of genetic networks in response to DNA damage. Science, 330, 1385-1389.
4. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate-a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Methadol., 57, 289-300.
5. Chuang, H.Y. et al. (2007) Network-based classification of breast cancer metastasis. Mol. Syst. Biol., 3, 140.
6. de Lichtenberg, U. et al. (2005) Dynamic complex formation during the yeast cell cycle. Science, 307, 724-727.
7. Dehmer, D.M. and Mowshowitz, A. (2011) A history of graph entropy measures. Inf. Sci., 181, 57-78.
8. DeLong, E.R. et al. (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 44, 837-845.
9. Doering, T.A. et al. (2012) Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory. Immunity, 37, 1130-1144.
10. Edge, S.B. et al. (2010) AJCC Cancer Staging Manual. Springer.
11. Fortunato, S. and Barthelemy, M. (2007) Resolution limit in community detection. Proc. Natl Acad. Sci. USA, 104, 36-41.
12. Goh, K.I. et al. (2007) The human disease network. Proc. Natl Acad. Sci. USA, 104, 8685-8690.
13. Han, J.D. et al. (2004) Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature, 430, 88-93.
14. He, D. et al. (2012) Coexpression network analysis in chronic hepatitis B and C hepatic lesions reveals distinct patterns of disease progression to hepatocellular carcinoma. J. Mol. Cell Biol., 4, 140-152.
15. Hernandez-Alcoceba, R. et al. (2000) The Ras family of GTPases in cancer cell invasion. Cell. Mol. Life Sci., 57, 65-76.
16. Hu, H.Y. et al. (2005) Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics, 21, I213-I221.
17. Huang, Y. et al. (2007) Systematic discovery of functional modules and contextspecific functional annotation of human genome. Bioinformatics, 23, I222-I229.
18. Kanehisa, M. et al. (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res., 40, D109-D114.
19. Kelley, B.P. et al. (2003) Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc. Natl Acad. Sci. USA, 100, 11394-11399.
20. Koyuturk, M. et al. (2004) An efficient algorithm for detecting frequent subgraphs in biological networks. Bioinformatics, 20, 200-207.
21. Lancichinetti, A. and Fortunato, S. (2012) Consensus clustering in complex networks. Sci. Rep., 2, 336.
22. Langfelder, P. and Horvath, S. (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Syst. Biol., 1, 54.
23. Lee, I. et al. (2011) Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res., 21, 1109-1121.
24. Lee, M.J. et al. (2012) Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell, 149, 780-794.
25. Letunic, I. et al. (2006) SMART 5: domains in the context of genomes and networks. Nucleic Acids Res., 34, D257-D260.
26. Li, W. et al. (2011) Integrative analysis of many weighted co-expression networks using tensor computation. PLoS Comput. Biol., 7, e1001106.
27. Masica, D.L. and Karchin, R. (2011) Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival. Cancer Res., 71, 4550-4561.
28. Narayanan, M. et al. (2010) Simultaneous clustering of multiple gene expression and physical interaction datasets. PLoS Comput. Biol., 6, e1000742.
29. Newman, M.E. (2006a) Modularity and community structure in networks. Proc. Natl Acad. Sci. USA, 103, 8577-8582.
30. Newman, M.E.J. (2006b) Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E, 74, 036104.
31. Nguyen, D.X. et al. (2009) Metastasis: from dissemination to organ-specific colonization. Nat. Rev. Cancer, 9, 274-284.
32. Nishimura, D. (2001) BioCarta. Biotech. Softw. Internet Rep., 2, 117-120.
33. Palla, G. et al. (2007) Quantifying social group evolution. Nature, 446, 664-667.
34. Pleasance, E.D. et al. (2010) A comprehensive catalogue of somatic mutations from a human cancer genome. Nature, 463, 191-196.
35. Subramanian, A. et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl Acad. Sci. USA, 102, 15545-15550.
36. Taylor, I.W. et al. (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol., 27, 199-204.
37. Tomlins, S.A. et al. (2007) Integrative molecular concept modeling of prostate cancer progression. Nat. Genet., 39, 41-51.
38. Vanunu, O. et al. (2010) Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol., 6, e1000641.
39. Vogelstein, B. et al. (2013) Cancer genome landscapes. Science, 339, 1546-1558.
40. Zhang, B. et al. (2013) Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell, 153, 707-720.
41. Zhong, Q. et al. (2009) Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol., 5, 321.
42. Zhou, D. et al. (2004) Learning with local and global consistency. Adv. Neural Inf. Proc. Syst., 16, 321-328.