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Concept-based Detection of Functional Modules in Protein Interaction Networks  

Park, Jong-Min (한국전자통신연구원 라이프인포메틱스팀)
Choi, Jae-Hun (한국전자통신연구원 라이프인포메틱스팀)
Park, Soo-Jun (한국전자통신연구원 아이프인포매틱스팀)
Yang, Jae-Dong (전북대학교 전자정보공학부)
Publication Information
Journal of KIISE:Computer Systems and Theory / v.34, no.10, 2007 , pp. 474-492 More about this Journal
Abstract
In the protein interaction network, there are many meaningful functional modules, each involving several protein interactions to perform discrete functions. Pathways and protein complexes are the examples of the functional modules. In this paper, we propose a new method for detecting the functional modules based on concept. A conceptual functional module, briefly concept module is introduced to match the modules taking them as its instances. It is defined by the corresponding rule composed of triples and operators between the triples. The triples represent conceptual relations reifying the protein interactions of a module, and the operators specify the structure of the module with the relations. Furthermore, users can define a composite concept module by the counterpart rule which, in turn, is defined in terms of the predefined rules. The concept module makes it possible to detect functional modules that are conceptually similar as well as structurally identical to users' queries. The rules are managed in the XML format so that they can be easily applied to other networks of different species. In this paper, we also provide a visualized environment for intuitionally describing complexly structured rules.
Keywords
concept-based detection; triple-based rule definition; functional module; protein interaction network;
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1 Tucker CL, Gera JF, and Uetz P, 'Towards an understanding of complex protein networks,' Trends Cell Biol., Vol.11, No.3, pp.102-106, 2001   DOI   ScienceOn
2 Ito T, Chiba T, Ozawa R, et al., 'A comprehensive two-hybrid analysis to explore the yeast protein interactome,' Proc. Natl Acad. Sci., Vol.98, No.8, pp.4569-4574, 2001   DOI   ScienceOn
3 Ho Y, Gruhler A, Heilbut A, et al., 'Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry,' Nature, 415, No.6868, pp.180-183, 2002   DOI   ScienceOn
4 Dogrusoz U, Erson EZ, Giral E, et al., 'PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization,' Bioinformatics, Vol.22, No.3, pp.374-375, 2006   DOI   ScienceOn
5 Lubovac Z, Gamalielsson J, Olsson B., 'Combining functional and topological properties to identify core modules in protein interaction networks,' Proteins, Vol. 64, No. 4, pp.948-959, 2006   DOI   ScienceOn
6 Baitaluk M, Qian X, Godbole S, et al., 'PathSys: integrating molecular interaction graphs for systems biology,' BMC Bioinformatics, Vol.7, 2006
7 Boeckmann B, Bairoch A, Apweiler R, et al., 'The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003,' Nucleic Acids Res., Vol.31, No.1, pp.365-370, 2003   DOI
8 Gavin AC, Bosche M, Krause R, et al., 'Functional organization of the yeast proteome by systematic analysis of protein complexes,' Nature, Vol.415, No.6868, pp.141-147, 2002   DOI   ScienceOn
9 Joshi-Tope G, Gillespie M, Vastrik I, et al., 'Reactome: a knowledgebase of biological pathways,' Nucleic Acids Res., Vol.33, Database issue, pp.D428-D432, 2005   DOI
10 Rinaldi F, Schneider G, Kaljurand K, et al., 'An environment for relation mining over richly annotated corpora: the case of GENIA,' BMC Bioinformatics, Vol.7, Suppl.3, Article S3, 2006
11 Kanehisa M, Goto S, Kawashima S, et al., 'The KEGG resource for deciphering the genome,' Nucleic Acids Res., Vol.32, Database issue, pp.D277-D280, 2004   DOI   ScienceOn
12 Koyuturk M, Grama A, and Szpankowski W., 'An efficient algorithm for detecting frequent subgraphs in biological networks,' Bioinformatics, Vol.20, Suppl. 1, pp.i200-i207, 2004   DOI
13 Zhang S, Ning X, Zhang XS., 'Identification of functional modules in a PPI network by clique percolation clustering,' Computational Biology and Chemistry, Vol.30, No. 6, pp.445-451, 2006   DOI   ScienceOn
14 Li XL, Tan SH, Foo CS, et al., 'Interaction Graph Mining for Protein Comple Merging,' Genome Informatics, Vol.16, No.2, pp.260-269, 2005
15 Bader GD and Hogue CW., 'An automated method for finding molecular complexes in large protein interaction networks,' BMC Bioinformatics, Vol.4, Article 2, 2003
16 Zanzoni A, Montecchi-Palazzi L, Quondam M, et al., 'MINT: a Molecular INTeraction database,' FEBS Lett., Vol.513, No.1, pp.135-140, 2002   DOI   ScienceOn
17 Harris MA, Clark J, Ireland A, et al., 'The Gene Ontology (GO) database and informatics resource,' Nucleic Acids Res., Vol.32, Database issue, pp.D258-D261, 2004   DOI
18 Ravasz E, Somera AL, Mongru DA, et al., 'Hierarchical organization of modularity in metabolic networks,' Science, Vol.297, No.5586, pp.1551- 1555, 2002   DOI   ScienceOn
19 Bader GD, Betel D. and Hogue CW., 'BIND: the Biomolecular Interaction Network Database,' Nucleic Acids Res., Vol.31, No.1, pp.248-250, 2003   DOI
20 Yeast Protein Complex Database, http://yeast.cellzome.com/
21 Xenarios I, Salwinski L, Duan XJ, et al., 'DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions,' Nucleic Acids Res., Vol.30, No.1, pp.303-305, 2002   DOI
22 Leser U., 'A query language for biological networks,' Bioinformatics, Vol.21, Suppl.2, pp.ii33-ii39, 2005   DOI