Exploring the structure and function of temporal networks with dynamic graphlets

Bioinformatics

Volumn 31, Issue 12, 2015, Pages i171-i180

  Hulovatyy, Y ^a   Chen, H ^a   Milenkovic T ^a  

^a University of Notre Dame   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGING; BIOLOGICAL MODEL; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METABOLISM; PROTEIN ANALYSIS;

AGING; GENE EXPRESSION PROFILING; HUMANS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; PROTEIN INTERACTION MAPPING;

EID: 84931084189     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv227     Document Type: Conference Paper

References (58)

1. Artzy-Randrup Y., et al. (2004). Comment on Network motifs: simple building blocks of complex networks and Superfamilies of evolved and designed networks. Science, 305, 1107-1107
2. Ashburner M., et al. (2000). Gene Ontology: tool for the unification of biology. Nat. Genet., 25, 25-29
3. Bajardi P., et al. (2011). Dynamical patterns of cattle trade movements. PLoS One, 6, e19869
4. Berchtold N.C., et al. (2008). Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc. Natl Acad. Sci., 105, 15605-15610
5. Braha D. and Bar-Yam Y. (2009). Time-dependent complex networks: dynamic centrality, dynamic motifs, and cycles of social interactions. In: Adaptive Networks, Springer, Berlin, pp. 39-50
6. Chechik G., et al. (2008). Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network. Nat. Biotechnol., 26, 1251-1259
7. de Magalhães J., et al. (2009). The human ageing genomic resources: online databases and tools for biogerontologists. Aging Cell, 8, 65-72
8. Du P., et al. (2009). From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations. Bioinformatics, 25, 63-68
9. Faisal F.E. and Milenković, T. (2014). Dynamic networks reveal key players in aging. Bioinformatics, 30, 1721-1729
10. Faisal F., et al. (2014). Global network alignment in the context of aging. IEEE/ACM Trans. Comput. Biol. Bioinform., 23
11. Ferrarini L., et al. (2005). A more efficient search strategy for aging genes based on connectivity. Bioinformatics, 21, 338-348
12. Ho H., et al. (2010). Protein interaction network uncovers melanogenesis regulatory network components within functional genomics datasets. BMC Syst. Biol., 4, 84
13. Hočevar T. and Demšar J. (2014). A combinatorial approach to graphlet counting. Bioinformatics, 30, 559-565
14. Holme P. and Saramäki J. (2012). Temporal networks. Phys. Rep., 519, 97-125
15. Hsieh M.-F. and Sze S.-H. (2014). Finding alignments of conserved graphlets in protein interaction networks. J. Comput. Biol., 21, 234-246
16. Hulovatyy Y., et al. (2014a) Network analysis improves interpretation of affective physiological data. J. Complex Netw., 2, 614-636
17. Hulovatyy Y., et al. (2014b) Revealing missing parts of the interactome via link prediction. PLoS One, 9, e90073
18. Jurgens D. and Lu T.-C. (2012). Temporal motifs reveal the dynamics of editor interactions in Wikipedia. In: The 6th International AAAI Conference on Weblogs and Social Media, Dublin, Ireland, June 4-7
19. Kato T. and Kato N. (2000). Mitochondrial dysfunction in bipolar disorder. Bipolar Disord., 2, 180-190
20. Kovanen L., et al. (2011). Temporal motifs in time-dependent networks. J. Stat. Mech.: Theory Exp., 2011, P11005
21. Kovanen L., et al. (2013). Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. Proc. Natl Acad. Sci., 110, 18070-18075
22. Kriete A., et al. (2011). Computational systems biology of aging. Wiley Interdisciplinary Rev.: Syst. Biol. Med., 3, 414-428
23. Kuchaiev O., et al. (2010). Topological network alignment uncovers biological function and phylogeny. J. R. Soc. Interface, 7, 1341-1354
24. Kuchaiev O., et al. (2011). GraphCrunch 2: software tool for network modeling, alignment and clustering. BMC Bioinformatics, 12, 24
25. Leskovec J., et al. (2005). Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, pp. 177-187
26. Leskovec J., et al. (2008). Microscopic evolution of social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, pp. 462-470
27. Lugo-Martinez J. and Radivojac P. (2014). Generalized graphlet kernels for probabilistic inference in sparse graphs. Netw. Sci., 2, 254-276
28. Malod-Dognin N. and Pržulj N. (2014). GR-Align: fast and flexible alignment of protein 3D structures using graphlet degree similarity. Bioinformatics, 30, 1259-1265
29. Marcus D. and Shavitt Y. (2012). RAGE-a rapid graphlet enumerator for large networks. Comput. Netw., 56, 810-819
30. Milenković, T. and Pržulj N. (2008). Uncovering biological network function via graphlet degree signatures. Cancer Inform., 6, 257-273
31. Milenković, T., et al. (2008). GraphCrunch: a tool for large network analyses. BMC Bioinformatics, 9, 70
32. Milenković, T., et al. (2009). Optimized null model for protein structure networks. PLoS One, 4, e5967
33. Milenković, T., et al. (2010a) Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data. J. R. Soc. Interface, 7, 423-437
34. Milenković, T., et al. (2010b) Optimal network alignment with graphlet degree vectors. Cancer Inform., 9, 121
35. Milenković, T., et al. (2011). Dominating biological networks. PLoS One, 6, e23016
36. Milo R., et al. (2002). Network motifs: simple building blocks of complex networks. Science, 298, 824-827
37. Milo R., et al. (2004). Superfamilies of evolved and designed networks. Science, 303, 1538-1542
38. Newman M. (2010). Networks: An Introduction. Oxford University Press, New York
39. Nicosia V., et al. (2012). Components in time-varying graphs. Chaos, 22, 3101
40. Peri S., et al. (2004). Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res., 32, 497-501
41. Priebe C.E., et al. (2005). Scan statistics on Enron graphs. Comput. Math. Org. Theory, 11, 229-247
42. Pržulj N. (2007). Biological network comparison using graphlet degree distribution. Bioinformatics, 23, e177-e183
43. Pržulj N. (2011). Protein-protein interactions: making sense of networks via graph-theoretic modeling. BioEssays, 33, 115-123
44. Pržulj N., et al. (2004). Modeling interactome: scale-free or geometric?. Bioinformatics, 20, 3508-3515
45. Pržulj N., et al. (2010). Geometric evolutionary dynamics of protein interaction networks. In: Pacific Symposium on Biocomputing, vol. 2009, World Scientific Press, Singapore, pp. 178-189
46. Przytycka T.M., et al. (2010). Toward the dynamic interactome: it's about time. Brief. Bioinform., 11, 15-29
47. Rahman M., et al. (2014). GRAFT: an efficient graphlet counting method for large graph analysis. IEEE Trans. Knowl. Data Eng., 26, 2466-2478
48. Saraph V. and Milenković, T. (2014). MAGNA: maximizing accuracy in global network alignment. Bioinformatics, 30, 2931-2940
49. Simon N.M., et al. (2006). Telomere shortening and mood disorders: preliminary support for a chronic stress model of accelerated aging. Biol. Psychiat., 60, 432-435
50. Singh O., et al. (2014). Graphlet signature-based scoring method to estimate protein-ligand binding affinity. R. Soc. Open Sci., 1, 140306
51. Solava R.W., et al. (2012). Graphlet-based edge clustering reveals pathogen-interacting proteins. Bioinformatics, 28, 480-486
52. Vacic V., et al. (2010). Graphlet kernels for prediction of functional residues in protein structures. J. Comput. Biol., 17, 55-72
53. Valencia M., et al. (2008). Dynamic small-world behavior in functional brain networks unveiled by an event-related networks approach. Phys. Rev., 77, 050905
54. Vazquez A., et al. (2001). Modeling of protein interaction networks. Complex Syst., 1, 38
55. Wang X.-D., et al. (2014). Identification of human disease genes from interactome network using graphlet interaction. PLoS One, 9, e86142
56. Wong S.W., et al. (2014). Comparative network analysis via differential graphlet communities. Proteomics, 15, 608-617
57. Yaverog?lu, Ö.N., et al. (2014). Revealing the hidden language of complex networks. Sci. Rep., 4, 4547
58. Zhao Q., et al. (2010). Communication motifs: a tool to characterize social communications. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, ACM Press, pp. 1645-1648