Abstract
Automatic discovery of functional complexes from protein interaction data is a rewarding but challenging problem. While previous approaches use approximations to extract dense modules, our approach exactly solves the problem of dense module enumeration. Furthermore, constraints from additional information sources such as gene expression and phenotype data can be integrated, so we can systematically detect dense modules with interesting profiles. Given a weighted protein interaction network, our method discovers all protein sets that satisfy a user-defined minimum density threshold. We employ a reverse search strategy, which allows us to exploit the density criterion in an efficient way.
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References
Sharan R, Ulitsky I, Shamir R (2007) Network-based prediction of protein function. Mol Syst Biol 3:88
Ulitsky I, Shamir R (2007) Identification of functional modules using network topology and high-throughput data. BMC Syst Biol 1:8
Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2
Uno T (2007) An efficient algorithm for enumerating pseudo cliques. In: Proceedings of ISAAC 2007, pp. 402–414
Chen J, Yuan B (2006) Detecting functional modules in the yeast protein-protein interaction network. Bioinformatics 22(18):2283–2290
van Dongen S (2000) Graph clustering by flow simulation. PhD thesis, University of Utrecht
Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103(23):8577–8582
Everett L, Wang LS, Hannenhalli S (2006) Dense subgraph computation via stochastic search: application to detect transcriptional modules. Bioinformatics 22(14):e117–e123
Palla G, Derenyi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818
Spirin V, Mirny LA (2003) Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci USA 100(21):12123–12128
Zeng Z, Wang J, Zhou L, Karypis G (2006) Coherent closed quasi-clique discovery from large dense graph databases. KDD '06: proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 797–802
Hanisch D, Zien A, Zimmer R, Lengauer T (2002) Co-clustering of biological networks and gene expression data. Bioinformatics 18(suppl 1):S145–S154
Tanay A, Sharan R, Kupiec M, Shamir R (2004) Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc Natl Acad Sci USA 101(9):2981–2986
Segal E, Wang H, Koller D (2003) Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19(suppl 1):i264–i271
Pei J, Jiang D, Zhang A (2005) Mining cross-graph quasi-cliques in gene expression and protein interaction data. ICDE '05: proceedings of the 21st international conference on data engineering (ICDE'05). IEEE Computer Society, Washington, DC, pp 353–354
Ideker T, Ozier O, Schwikowski B, Siegel AF (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(suppl 1):S233–S240
Huang Y, Li H, Hu H, Yan X, Waterman MS, Huang H, Zhou XJ (2007) Systematic discovery of functional modules and context-specific functional annotation of human genome. Bioinformatics 23(13):i222–i229
Yan X, Mehan MR, Huang Y, Waterman MS, Yu PS, Zhou XJ (2007) A graph-based approach to systematically reconstruct human transcriptional regulatory modules. Bioinformatics 23(13):i577–i586
Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, Margalit H, Armstrong J, Bairoch A, Cesareni G, Sherman D, Apweiler R (2004) IntAct: an open source molecular interaction database. Nucleic Acids Res 32(suppl 1):D452–D455
Chatr-aryamontri A, Ceol A, Palazzi LM, Nardelli G, Schneider MV, Castagnoli L, Cesareni G (2007) MINT: the Molecular INTeraction database. Nucleic Acids Res 35(suppl 1):D572–D574
Bader GD, Betel D, Hogue CWV (2003) BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res 31(1):248–250
Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G, Cooke MP, Walker JR, Hogenesch JB (2004) A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A 101(16):6062–6067
Avis D, Fukuda K (1996) Reverse search for enumeration. Discrete Appl Math 65:21–46
Han J, Kamber M (2006) Data mining: concepts and techniques of the Morgan Kaufmann series in data management systems, 2nd edn. Morgan Kaufmann Publishers, San Francisco
Georgii E, Dietmann S, Uno T, Pagel P, Tsuda K (2009) Enumeration of condition-dependent dense modules in protein interaction networks. Bioinformatics 25:933–940
Georgii E, Tsuda K, Schölkopf B (2011) Multi-way set enumeration in weight tensors. Mach Learn 82:123–155
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Tsuda, K., Georgii, E. (2013). Dense Module Enumeration in Biological Networks. In: Mamitsuka, H., DeLisi, C., Kanehisa, M. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 939. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-107-3_1
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DOI: https://doi.org/10.1007/978-1-62703-107-3_1
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