Merging network patterns: A general framework to summarize biomedical network data

Network Modeling and Analysis in Health Informatics and Bioinformatics

Volumn 1, Issue 3, 2012, Pages 103-116

  Xiang, Yang ^a   Fuhry, David ^b   Kaya, Kamer ^a   Jin, Ruoming ^c   Çatalyürek, Ümit V ^a   Huang, Kun ^a  

^a OHIO STATE UNIVERSITY   (United States)

^b Ohio State University   (United States)

^c KENT STATE UNIVERSITY   (United States)

Author keywords

Bicliques; Cliques; Frequent itemset mining; Graph theory; Network patterns; Summarization

Indexed keywords

ALGORITHM; ARTICLE; BIOINFORMATICS; DNA MICROARRAY; GENE EXPRESSION; HUMAN; NETWORK LEARNING; PHENOTYPE; PRIORITY JOURNAL; PROGNOSIS; SURVIVAL;

EID: 84899757219     PISSN: None     EISSN: 21926670     Source Type: Journal    
DOI: 10.1007/s13721-012-0009-3     Document Type: Article

References (51)

1. Abello J, Resende MGC, Sudarsky S (2002) Massive quasi-clique detection. In: LATIN, pp 598-612.
2. Almansoori W, Gao S, Jarada TN, Elsheikh AM, Murshed AN, Jida J, Alhajj R, Rokne J (2012) Link prediction and classification in social networks and its application in healthcare and systems biology. Netw Model Anal Health Inform Bioinforma. doi: 10. 1007/s13721-012-0005-7.
3. Bastian M, Heymann S, Gephi MJ (2009) An open source software for exploring and manipulating networks. In: ICWSM.
4. Berkhin P (2006) A survey of clustering data mining techniques. Grouping Multidimensional Data.
5. Bron C, Kerbosch J (1973) Algorithm 457: finding all cliques of an undirected graph. Commun ACM 16(9): 575-577.
6. Burdick D, Calimlim M, Flannick J, Gehrke J, Yiu T (2005) Mafia: a maximal frequent itemset algorithm. IEEE Trans Knowl Data Eng 17(11): 1490-1504.
7. Carroll JS, Meyer CA, Song J, Li W et al (2006) Genome-wide analysis of estrogen receptor binding sites. Nat Genet 38(11): 1289-1297.
8. Defays D (1977) An efficient algorithm for a complete link method. Comput J 20(4): 364-366.
9. Desmedt C, Haibe-Kains B, Wirapati P et al (2008) Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res 14(16): 5158-5165.
10. Du N, Wang B, Wu B, Wang Y (2008) Overlapping community detection in bipartite networks. In: Web intelligence, pp 176-179.
11. Duan R, Pettie S, Su H-H (2011) Scaling algorithms for approximate and exact maximum weight matching.
12. Edmonds J (1965) Paths, trees, and flowers. Can J Math 17(3): 449-467.
13. Geerts F, Goethals B, Mielikainen T (2004) Tiling databases. In: Discovery science, pp 278-289.
14. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Elsevier.
15. Hartigan JA (1972) Direct clustering of a data matrix. J Am Stat Assoc 67(337): 123-129.
16. Jin R, Hong H, Wang H, Ruan N, Xiang Y (2010) Computing label-constraint reachability in graph databases. In: SIGMOD conference, pp 123-134.
17. Jin R, Ruan N, Xiang Y, Lee VE (2012) A highway-centric labeling approach for answering distance queries on large sparse graphs. In: SIGMOD conference.
18. Jin R, Xiang Y, Hong H, Huang K (2010) Block interaction: a generative summarization scheme for frequent patterns. In: UP '10 proceedings of the ACM SIGKDD workshop on useful patterns, pp 55-64.
19. Johnson DS, Yannakakis M, Papadimitriou CH (1988) On generating all maximal independent sets. Inf Process Lett 27(3): 119-123.
20. Karp R (1972) Reducibility among combinatorial problems. In: Miller R, Thatcher J (eds) Complexity of computer computations. Plenum Press, New York, pp 85-103.
21. Kutalik Z, Beckmann JS, Bergmann S (2008) A modular approach for integrative analysis of large-scale gene-expression and drug-response data. Nat Biotechnol 26(5): 531-539.
22. Li J, Liu Y, Gao H (2011) Efficient algorithms for summarizing graph patterns. IEEE Trans Knowl Data Eng 23(9): 1388-1405.
23. Li J, Liu G, Li H, Wong L (2007) Maximal biclique subgraphs and closed pattern pairs of the adjacency matrix: a one-to-one correspondence and mining algorithms. IEEE Trans Knowl Data Eng 19(12): 1625-1637.
24. Li J, Sim K, Liu G, Wong L (2008) Maximal quasi-bicliques with balanced noise tolerance: concepts and co-clustering applications. In: SDM, pp 72-83.
25. Lucchese C, Orlando S, Perego R (2010) A generative pattern model for mining binary datasets. In: SAC, pp 1109-1110.
26. Makino K, Uno T (2004) New algorithms for enumerating all maximal cliques. In: SWAT, pp 260-272.
27. Micali S, Vazirani VV (1980) An O(√{pipe}V{pipe}{pipe}E{pipe}) algorithm for finding maximum matching in general graphs. In: FOCS, pp 17-27.
28. Miettinen P, Mielikäinen T, Gionis A, Das G, Mannila H (2008) The discrete basis problem. IEEE Trans Knowl Data Eng 20(10): 1348-1362.
29. Mirkin B (1996) Mathematical classification and clustering. Kluwer Academic Publishers, Dordrecht.
30. Moon JW, Moser L (1965) On cliques in graphs. Israel J Math 3(1): 23-28.
31. Mucha M, Sankowski P (2004) Maximum matchings via gaussian elimination. In: FOCS, pp 248-255.
32. Mushlin RA, Gallagher S, Kershenbaum A, Rebbeck TR (2009) Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the chamber algorithm. PloS one 4(3): 4862.
33. Ou Y, Zhang C (2007) A new multimembership clustering method. J Ind Manag Optim 3(4): 619.
34. Ravetti MG, Moscato P (2008) Identification of a 5-protein biomarker molecular signature for predicting alzheimer's disease. PLoS One 3(9): 3111.
35. Reyal F, van Vliet MH, Armstrong NJ et al (2008) A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and rna splicing modules in breast cancer. Breast Cancer Res 10(6): R93.
36. Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S (2008) The human phenotype ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet 83(5): 610-615.
37. Robinson PN, Mundlos S (2010) The human phenotype ontology. Clin Genet 77(6): 525-534.
38. Seidman SB (1983) Network structure and minimum degree* 1. Social Netw 5(3): 269-287.
39. Seidman SB, Foster BL (1978) A graph-theoretic generalization of the clique concept. J Math Sociol 6(1): 139-154.
40. Slink RS (1973) An optimally efficient algorithm for the single-link cluster method. Comput J 16(1): 30-34.
41. Stoer M, Wagner F (1997) A simple min-cut algorithm. J ACM 44(4): 585-591.
42. Tsukiyama S, Ide M, Ariyoshi H, Shirakawa I (1977) A new algorithm for generating all the maximal independent sets. SIAM J Comput 6: 505.
43. Uppalapati P, Xiang Y, Huang K (2010) Predicting prognostic markers for glioma using gene co-expression network analysis. In: Proceedings of the first ACM international conference on bioinformatics and computational biology, pp 546-551.
44. van de Vijver MJ, He YD, van 't Veer LJ et al (2002) A gene-expression signature as a predictor of survival in breast cancer. New Engl J Med 347(25): 1999-2009.
45. van't Veer LJ, Dai H, van de Vijver MJ et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871): 530-536.
46. Wang Y, Klijn PGM, Zhang Y et al (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365(9460): 671-679.
47. Wirapati P, Sotiriou C, Kunkel S et al (2008) Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res 10(4): R65.
48. Xiang Y, Zhang CQ, Huang K (2012) Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on tcga data. BMC Bioinform 13(Suppl 2): S12.
49. Xiang Y, Jin R, Fuhry D, Dragan FF (2011) Summarizing transactional databases with overlapped hyperrectangles. Data Min Knowl Discov 23(2): 215-251.
50. Xiang Y, Payne P, Huang K (2012) Transactional database transformation and its application in prioritizing human disease genes. IEEE/ACM Trans Comput Biol Bioinform 9(1): 294-304.
51. Zhang J, Xiang Y, Ding L et al (2010) Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia. BMC Bioinform 11(Suppl 9): S5.