Vol. 37 ( 2017 ) No. 5. J. of Math. (PRC) : A : (2017) , DNA,

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7 No. 5 : 199. [1] Aebersold R, Mann M. Mass spectrometry-based proteomics[j]. Nature, 23, 422(6928): [2] Ho Y, Gruhler A, Heilbut A, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry[j]. Nature, 22, 415(6868): [3] Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome[j]. Proceed. National Acad. Sci. United States America, 21, 98(8): [4] Uetz P, Giot L, Cagney G, Mansfield T A, et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae[j]. Nature, 2, 43(677): [5] Gavin A C, B sche M, Krause R, et al. Functional organization of the yeast proteome by systematic analysis of protein complexes[j]. Nature, 22, 415(6868): [6] Lage K, Karlberg E O, Størling Z M, et al. A human phenome-interactome network of protein complexes implicated in genetic disorders[j]. Nature Biotechnology, 27, 25(3): [7] Lo K, Raftery A E, Dombek K M, et al. Integrating external biological knowledge in the construction of regulatory networks from time-series expression data[j]. BMC Sys. Bio., 212, 6(2): 11. [8] Vasmatzis G, Klee E W, Kube D M, Therneau T M, Kosari F. Quantitating tissue specificity of human genes to facilitate biomarker discovery[j]. Bioinformatics, 27, 23(11): [9] Li D, Li J, Ouyang S, Wang J, Wu S, Wan P, Zhu Y, Xu X, He F. Protein interaction networks of Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster: large-scale organization and robustness[j]. Proteomics, 26, 6(2): [1] Enright A J, Dongen S V, Ouzounis C A. An efficient algorithm for largescale detection of protein families[j]. Nucleic Acids Res, 212, 3(7): [11],,,. [J]., 214, 4(4): [12] Bader G D, Hogue C W V. An automated method for finding molecular complexes in large protein interaction networks[j]. BMC Bioinformatics, 23, 4(1): 2. [13] Rhrissorrakrai K, Gunsalus K C. MINE: module identification in networks[j]. BMC Bioinformatics, 211, 12(1): 192. [14] Altaf-Ul-Amin M, Shinbo Y, Mihara K, Kurokawa K, Kanaya S. Development and implementation of an algorithm for detection of protein complexes in large interaction networks[j]. BMC Bioinformatics, 26, 7(1): 27. [15] Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks[j]. Nature Methods, 212, 9(5): [16] Wu M, Li X L, Kwoh C K, Ng C K. A core-attachment based method to detect protein complexes in PPI networks[j]. BMC Bioinformatics, 29, 1(1): 169. [17] Jiang P, Singh M. SPICi: a fast clustering algorithm for large biological networks[j]. Bioinformatics, 21, 26(8): [18] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[j]. Nature, 1999, 41(6755): [19] Ding C, He X F, Simon H D. On the equivalence of nonnegative matrix factorization and spectral clustering[j]. Siam Intern. Confer. Data Min., 25, 5:

8 11 Vol. 37 [2] Lopes T J, Schaefer M, Shoemaker J, Matsuoka Y, Fontaine J F, Neumann G, Andrade-Navarro M A, Kawaoka Y, Kitano H. Tissue-specific subnetworks and characteristics of publicly available human protein interaction databases[j]. Bioinformatics, 211, 27(17): [21] Chatr-aryamontri A, Breitkreutz B J, Heinicke S, et al. The Biogrid interaction database: 213 update[j]. Nucleic Acids Research, 213, 41(2): [22] Havugimana P C, Hart G T, Nepusz T, et al. A census of human soluble protein complexes[j]. Cell, 212, 15(5): [23] Li X, Wu M, Kwoh C K, et al. Computational approaches for detecting protein complexes from protein interaction networks: a survey[j]. BMC Genomics, 21, 11(4): S3. [24] Ou-Yang L, Dai D Q, Zhang X F. Protein complex detection via weighted ensemble clustering based on bayesian nonnegative matrix factorization[j]. Plos One, 213, 8(5): [25] Ou-Yang L, Dai D Q, Li X L, Wu M, Zhang X F, Yang P. Detecting temporal protein complexes from dynamic protein-protein interaction networks[j]. BMC Bioinformatics, 214, 15(1): [26] Zhang X F, Dai D Q, Ou-Yang L, Yan H. Detecting overlapping protein complexes based on a generative model with functional and topological properties[j]. BMC Bioinformatics, 214, 15(2): [27] Zhang W, Zou X F. A new method for detecting protein complexes based on the three node cliques[j]. IEEE/ACM Trans Comput. Biol. Bioinform, 215, 12(4): [28]. [J]., 26, 26(1): IDENTIFICATION OF THE TISSUE SPECIFIC PROTEIN COMPLEXES DING Xia 1, ZHANG Xiao-fei 2, YI Ming 3 (1.School of Mathematics and Statistics, Wuhan University, Wuhan 4372,China) (2.School of Mathematics and Statistics, Central China Normal University, Wuhan 4379, China) (3.School of Science, Huazhong Agricultural University, Wuhan 437, China) Abstract: In this paper, we study the identification problem of tissue-specific protein complexes. By using a variety of typical clustering algorithm to cluster the network, we construct a tissue-specific protein-protein interaction network based on the protein-protein interaction networks as well as the tissue-specific gene expression data, then merge the results with non-negative matrix factorization model to obtain tissue-specific protein complexes. The results show that clustering effect has been significantly improved, and can identify tissue-specific protein complexes. Keywords: protein-protein interaction networks; complexes identification; tissue-specific; non-negative matrix factorization 21 MR Subject Classification: 92B5