Clustering algorithms for detecting functionals modules in protein interaction networks

Journal of Bioinformatics and Computational Biology

Volumn 7, Issue 1, 2009, Pages 217-242

  Gao, Lin ^a   Sun, Peng Gang ^a   Song, Jia ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Clustering algorithms; Functional modules; Protein complexes; Protein protein interaction (PPI) networks

Indexed keywords

ACCURACY; ALGORITHM; CELL FUNCTION; CLUSTER ANALYSIS; COMPLEX FORMATION; PREDICTION; PROBABILITY; PROTEIN PROTEIN INTERACTION; REVIEW; VALIDATION PROCESS;

ALGORITHMS; CLUSTER ANALYSIS; MULTIGENE FAMILY; PATTERN RECOGNITION, AUTOMATED; PROTEIN INTERACTION MAPPING; PROTEOME; SIGNAL TRANSDUCTION;

EID: 65449134296     PISSN: 02197200     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219720009004023     Document Type: Review

References (97)

1. Hartwell LH et al., From molecular to modular cell biology, Nature 402:C47-C52, 1999.
2. Barabasi AL, Oltvai ZN, Network biology: Understanding the cell's functional organization, Nature Reviews Genetics 5 (2):101-114, 2004.
3. Ravasz E et al., Hierarchical organization of modularity in metabolic networks, Science 297(5586):1551-1555, 2002.
4. Rives AW, Galitski T, Modular organization of cellular networks, Proc Natl Acad Sci USA 100(3): 1128-1133, 2003.
5. Xenarios I et al., DIP, the database of interacting proteins: A research tool for studying cellular networks of protein interactions, Nucleic Acids Res 30(1):303-305, 2002.
6. Brandes U et al., On modularity clustering, IEEE Trans Knowledge and Data Engineering 20(2):172-188, 2008.
7. Altaf-Ul-Amin M, Shinbo Y, Mihara K et al., Development and implementation of an algorithm for detection of protein complexes in large interaction networks, BMC Bioinformatics 7(1):207, 2006.
8. Sharan R, Ideker T, Kelley B et al., Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data, J Comput Biol 12(6):835-846, 2005.
9. Gagneur J, Krause R, Bouwmeester T et al., Modular decomposition of protein-protein interaction networks, Genome Biology 5 (8):R57, 2004.
10. Cho YR, Hwang W, Ramanathan M et al., Semantic integration to identify overlapping functional modules in protein interaction networks, BMC Bioinformatics 8:1-13, 2007.
11. Boccaletti S, Ivanchenko M, Latora V et al., Detecting complex network modularity by dynamical clustering, Phys Rev E 75 045102, 2007.
12. Zhang S, Liu HW, Ning XM et al., A graph-theoretic method for mining functional modules in large sparse protein interaction networks, in Proceeding of Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), IEEE Computer Society Press, Los Alamitos, pp. 130-135, 2006.
13. Přzulj N, Analyzing Large Biological Networks: Protein-protein interactions example, Ph.D. thesis, University of Toronto, Canada, 2005.
14. Spirin V, Mirny LA, Protein complexes and functional modules in molecular networks, Proc Natl Acad Sci USA 100 (21):12123-12128, 2003.
15. Bader GD, Hogue CW, An automated method for finding molecular complexes in large protein interaction networks, BMC Bioinformatics 4 (1):2, 2003.
16. Xiong H, He X, Ding C, Zhang Y, Kumar V, Holbrook SR, Identification of functional modules in protein complexes via hyperclique pattern discovery, Pac Symp Biocomp 10:221-232, 2005.
17. Chen JC, Yuan B, Detecting functional modules in the yeast protein-protein interaction network, Bioinformatics 22 (18):2283-2290, 2006.
18. Přzulj N, Wigle D, Jurisica I, Functional topology in a network of protein interactions, Bioinformatics 20(3):340-348, 2004.
19. Hartuv E, Shamir R, A clustering algorithm based on graph connectivity, Information Processing Letters 76(4):175-181, 2000.
20. Li W, Liu Y, Huang HC et al., Dynamical systems for discovering protein complexes and functional modules from biological networks, IEEE/ACM Trans Comput Biol Bioinform 4:233-250, 2007.
21. King AD, Přzulj N, Jurisica I, Protein complex prediction via cost-based clustering, Bioinformatics 20(17):3013-3020, 2004.
22. Arnau V, Mars S, Marin I, Iterative cluster analysis of protein interaction data, Bioinformatics 21(3):364-378, 2005.
23. Van Dongen S, Graph clustering by flow simulation, Ph.D. thesis, Centers for Mathematics and Computer Science (CWI), University of Utrecht, 2000.
24. Enright AJ, Dongen SV, Ouzounis CA, An efficient algorithm for large-scale detection of protein families, Nucleic Acids Res 30 (7):1575-1584, 2002.
25. Pereira-Leal JB, Enright AJ, Ouzounis CA, Detection of functional modules from protein interaction networks, Proteins 54 (1):49-57, 2004.
26. Girvan M, Newman MEJ, Community structure in social and biological networks, Proc Natl Acad Sci USA 99:7821-7826, 2002.
27. Newman MEJ, Fast algorithm for detecting community structure in networks, Phys Rev E 69(6):066133, 2004.
28. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D, Defining and identifying communities in networks, Proc Natl Acad Sci USA 101 (9):2658-2663, 2004.
29. Luo F, Yang YF, Chen CF et al., Modular organization of protein interaction networks, Bioinformatics 23(2):207-214, 2007.
30. Dunn R, Dudbridge F, Sanderson CM, The use of edge-betweenness clustering to investigate biological function in protein interaction networks, BMC Bioinformatics 6:39, 2005.
31. Jeongah Y, Anselm B, Kyongbum L, An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality, Bioinformatics 22(24):3106-3108, 2006.
32. Brohee S, van Helden J, Evaluation of clustering algorithms for protein-protein interaction networks, BMC Bioinformatics 7 1-19, 2006.
33. Blatt M, Wiseman S, Domany E, Superparamagnetic clustering of data, Phys Rev Lett 76:3251-3254, 1996.
34. Palla G, Derényi I, Farkas IJ et al., Uncovering the overlapping community structure of complex networks in nature and society, Nature 435(18):814-818, 2005.
35. Adamcsek B, Palla G, Farkas IJ et al. CFinder: Locating cliques and overlapping modules in biological networks, Bioinformatics 22 (8):1021-1023, 2006.
36. Palla G, Derényi I, Farkas IJ et al., Uncovering the overlapping modular structure of protein interaction networks, FEBS J 272(Suppl. 1):434, 2005.
37. Zhang SH, Ning XM, Zhang XS, Identification of functional modules in a PPI network by clique percolation clustering, Comput Biol Chem 30(6):445-451, 2006.
38. Zhang C, Liu S, Zhou Y, Fast and accurate method for identifying high-quality protein-interaction modules by clique merging and its application to yeast, J Proteome Res 5:801-807, 2006.
39. Asur S, Ucar D, Parthasarathy S, An ensemble framework for clustering protein-protein interaction networks, Bioinformatics 23 (13):i29-40, 2007.
40. Rungsarityotin W, Krause R, Schodl A et al., Identifying protein complexes directly from high-throughput TAP data with Markov random fields, BMC Bioinformatics 8:482, 2007.
41. Li XL, Foo CS, Ng SK, Discovering protein complexes in dense reliable neighborhoods of protein interaction networks, Comput Syst Bioinformatics Conf 6:157-168, 2007.
42. Hon Nian Chua et al., Using indirect protein-protein interactions for protein complex predication, J Bioinform Comput Biol 6 (3):435-466, 2008.
43. Andreopoulos B, An A, Huang X, Wang X, Finding molecular complexes through multiple layer clustering of protein interaction networks, Int J Bioinform Res Appl 3(1):65-85, 2007.
44. Rui-Sheng W, Shihua Z, Xiang-Sun Z et al., Identifying modules in complex networks by a graph-theoretical method and its application in protein interaction networks, ICIC2007, Lecture Notes in Artificial Intelligence, pp.1090-1101, 2007.
45. Andreopoulos B, An A, Wang X et al., Clustering by common friends finds locally significant proteins mediating modules, Bioinformatics 23(9):1124-1131, 2007.
46. Michoel T, Maere S, Bonnet E et al., Validating module network learning algorithms using simulated data, BMC Bioinformatics 8 S5, 2007.
47. Tutte WT, Graph Theory, Cambridge University Press, Cambridge, 2001.
48. Přzulj N, Graph theory analysis of protein-protein interactions, in Jurisica I, Wigle DA (eds.), Knowledge Discovery in Proteomics, CRC Press, 2005.
49. Satu ES, Graph clustering, Computer Science Review 1 (1):27-64, 2007.
50. Blatt M, Wiseman S, Domany E, Superparamagnetic clustering of data, Phys Rev Lett 76(18):3251-3254, 1996.
51. Getz G, Levine E, Domany E, Coupled two-way clustering analysis of gene microarray data, Proc Natl Acad Sci USA 97(22):12079-12084, 2000.
52. Getz G, Vendruscolo M, Sachs D, Domany E, Automated assignment of SCOP and CATH protein structure classifications from FSSP scores, Proteins 46:405-415, 2002.
53. Gavin, AC, Bosche M, Krause R et al., Functional organization of the yeast proteome by systematic analysis of protein complexes, Nature 415(6868):141-147, 2002.
54. Mewes, HW et al., MIPS: Analysis and annotation of proteins from whole genomes, Nucleic Acids Res 32:D41-D44, 2004.
55. Lin C, Cho Y-R, Hwang W et al., Clustering methods in protein-protein interaction networks, in Hu X and Pan Y (eds.), Knowledge Discovery in Bioinformatics: Techniques, Methods and Application, Wiley, 2007.
56. Pei P, Zhang A, A topological measurement for weighted protein interaction network, in Proceedings of the IEEE Computer Society Bioinformatics Conference, pp. 268-278, 2005.
57. Zhou H, Distance, dissimilarity index, and network community structure, Phys Rev E 67:061901, 2003.
58. Zhou H, Network landscape from a Brownian particle's perspective, Phys Rev E 67:041908, 2003.
59. Newman MEJ, Fast algorithm for detecting community structure in networks, Phys Rev E 69:066133, 2004.
60. Guimera R et al., Functional cartography of complex metabolic networks, Nature 433(7028):895-900, 2005.
61. von Luxburg U, A tutorial on spectral clustering, Statistics and Computing 17(4):395-416, 2007.
62. Kannan R, Vempala S, Vetta A, On clusterings: Good, bad, and spectral, in Proceedings of the 41st Annual Symposium on the Foundation of Computer Science, IEEE Computer Society, Silver Spring, MD, pp. 367-380, 2000.
63. Shi J, Malik J, Normalized cuts and image segmentation, IEEE Trans Pattern Anal Mach Intell 22(8):888-905, 2000.
64. Xu, R, Wunsch DII, Survey of clustering algorithms, IEEE Trans Neural Networks 16(3):645-678, 2005.
65. Dhillon IS, Guan Y, Kulis B, Weighted graph cuts without eigenvectors: A multilevel approach, IEEE Trans Pattern Anal Mach Intell 29 (11):1944-1957, 2007.
66. Dhillon IS, Guan Y, Kulis B, A unified view of kernel κ-means, spectral clustering and graph partitioning, Technical Report, UTCS, TR-04-25, 2005.
67. Verma D, Meila M, A comparison of spectral clustering algorithms, Technical Report, Department of CSE University of Washington Seattle, WA, pp. 98195-92350, 2005.
68. Filipponea M, Camastrab F, Masullia F, Rovettaa S, A survey of kernel and spectral methods for clustering, Pattern Recognition 41 (1):176-190, 2008.
69. Bu D, Zhao Y, Cai L, Xue H et al., Topological structure analysis of the protein-protein interaction network in budding yeast, Nucleic Acids Res 31(9):2443-2450, 2003.
70. Newman MEJ, Finding community structure in networks using the eigenvectors of matrices, Phys Rev E 74:036104, 2006.
71. Boaz N, Meirav G, Fundamental limitations of spectral clustering, Neural Information Processing Systems 19:1017-1024, 2006.
72. Sharan R, Ulitsky I, Shamir R, Network-based prediction of protein function, Molecular Systems Biology 3(88):1-13, 2007.
73. Asthana S, King OD, Gibbons FD et al., Predicting protein complex membership using probabilistic network reliability, Genome Res 14(6):1170-1175, 2004.
74. Bader JS, Greedily building protein networks with confidence, Bioinformatics 19(15):1869-1874, 2003.
75. Wu DD, Hu X, An efficient approach to detect a protein community from a seed, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB2005), La Jolla, CA, USA, pp. 135-141, 2005.
76. Pei P, Zhang A, A "seed-refine" algorithm for detecting protein complexes from protein interaction data, IEEE Trans Nanobioscience 6(1):43-50, 2007.
77. Fred A, Jain AK, Data clustering using evidence accumulation, Proceedings of the 16th Int Conference on Pattern Recognition, pp. 276-280, 2002.
78. Topchy A et al., Analysis of consensus partition in cluster ensemble, Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04), pp. 225-232, 2004.
79. Eitan H, Roded S, Identification of conserved protein complexes based on a model of protein network evolution, Bioinformatics 23 (2):e170-e176, 2007.
80. Koyuturk M, Szpankowski W, Grama A, Assessing significance of connectivity and conservation in protein interaction networks, J Comput Biol 14:747-764, 2007.
81. Tan K, Shlomi T, Feizi H et al., Transcriptional regulation of protein complexes within and across species, Proc Natl Acad Sci USA 104(4):1283-1288, 2007.
82. Boccaletti S, Ivanchenko M, Latora V et al., Detecting complex network modularity by dynamical clustering, Phys Rev E 75 045102, 2007.
83. Weinan E, Tiejun Li, Vanden-Eijnden E, Optimal partition and effective dynamics of complex networks, Proc Natl Acad Sci USA 105 (23):7907-7912, 2008.
84. Rosvall M, Bergstrom CT, An information-theoretic framework for resolving community structure in complex networks, Proc Natl Acad Sci USA 104(18):7327-7331, 2007.
85. Martin R, Bergstrom CT, Maps of random walks on complex networks reveal community structure, Proc Natl Acad Sci USA 105:1118-1123, 2008.
86. Qi Y et al., Protein complex identification by supervised graph local clustering, Bioinformatics 24:250-258, 2008.
87. Zhang S et al., Prediction of protein complexes based on protein interaction data and functional annotation data using kernel methods, ICIC 2006, LNBI 4115, pp. 514-524, 2006.
88. Farkas I et al., Weighted network modules, New J Phys 9 180, 2007.
89. Chen P et al., Predicting and validating protein interactions using network structure, PLoS Computational Biology 4 (7):e1000118, 2008.
90. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y, A comprehensive two-hybrid analysis to explore the yeast protein interactome, Proc Natl Acad Sci USA 98(8):4569-4574, 2001.
91. Ho Y, Gruhler et al., Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry, Nature 415(6868):180-183, 2002.
92. Huh W et al., Global analysis of protein localization in budding yeast, Nature 425:686-691, 2003.
93. Jansen R, Gerstein M, Analyzing protein function on a genomic scale: The importance of gold-standard positives and negatives for network prediction, Curr Opin Microbiol 7:535-545, 2004.
94. Mewes H, Frishman D, Guldener U et al., Mips: A database for genomes and protein sequences, Nucleic Acids Res 30 (20):31-34, 2002.
95. Gavin AC et al., Proteome survey reveals modularity of the yeast cell machinery, Nature 440(7084):631-636, 2006.
96. Ruepp A et al., The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes, Nucleic Acids Res 32(18):5539-5545, 2004.
97. Chua HN, Sung W-K, Wong L, Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions, Bioinformatics 22(13): 1623-1630, 2006.