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A novel temporal protein complexes identification framework based on density–distance and heuristic algorithm

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
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Abstract

The construction of dynamic protein–protein interaction networks is affected by cell tissue and its biological function, and the identification of protein complexes is important for understanding biological functions. This paper presents a new method named density–distance and heuristic for identifying temporal protein complexes. First, the gene expression data of time course are integrated into the static protein interaction data and a set of time-ordered networks are obtained. Then, the network is integrated with the gene information to calculate the distance between proteins in the protein–protein interaction network. Based on this distance, we have formed a number of clusters and selected the furthest cluster from the other cluster centers as the initial cluster to ensure that nodes with clusters are closest to each other. Finally, a heuristic algorithm is introduced, and the initial cluster is updated in two ways. The experimental results show that the proposed method has better performance compared with the commonly used algorithms; meanwhile, this method has a strong biological significance.

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References

  1. Spirin V, Mirny LA (2003) Protein complexes and functional modules in molecular networks. Proc Nat Acad Sci USA 100(21):12123–12128

    Article  Google Scholar 

  2. Chen BL, Fan WW, Liu J, Wu FX (2014) Identifying protein complexes and functional modules-from static PPI networks to dynamic PPI networks. Brief Bioinform 15(2):177–194

    Article  Google Scholar 

  3. Bhowmick S, Seah BS (2016) Clustering and summarizing protein–protein interaction networks: a survey. IEEE Trans Knowl Data Eng 28(3):638–658

    Article  Google Scholar 

  4. Lv JW (2015) Research on function module detection from large-scale and dynamic PPI networks based on ant colony algorithm. Master Thesis. Beijing University of Technology, Beijing, China

  5. Li X, Wu M, Kwoh CK, Ng SK (2010) Computational approaches for detecting protein complexes from protein interaction networks: a survey. BMC Genom 11(Suppl 1):S3

    Article  Google Scholar 

  6. Lage K, Karlberg EO, Størling ZM, Olason PI, Pedersen AG, Rigina O (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25(3):309–316

    Article  Google Scholar 

  7. Przytycka TM, Singh M, Slonim DK (2010) Toward the dynamic interactome: it’s about time. Brief Bioinform 11:15–29

    Article  Google Scholar 

  8. Tang XW, Wang JX, Liu BB, Li M, Chen G, Pan Y (2011) A comparison of the functional modules identified from time course and static PPI network data. BMC Bioinform 12:339

    Article  Google Scholar 

  9. Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328:876–878

    Article  MathSciNet  Google Scholar 

  10. Wu XH (2013) Analysis and evaluation of clustering algorithm for the protein interaction networks. Master Thesis. Central South University, Changsha, China

  11. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM et al (2004) GO: TermFinder–open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 20(18):3710–3715

    Article  Google Scholar 

  12. Martin D, Brun C, Remy E, Mouren P, Thieffry D, Jacq B (2004) GOToolBox: functional analysis of gene datasets based on Gene Ontology. Genome Biol 5(12):60

    Article  Google Scholar 

  13. Glass K, Ott E, Losert W, Girvan M (2012) Implications of functional similarity for gene regulatory interactions. J R Soc Interface 9(72):1625–1636

    Article  Google Scholar 

  14. Yu YW, Zhao JD, Wang XD, Wang Q, Zhang YG (2015) Cludoop: an efficient distributed density-based clustering for big data using hadoop. Int J Distrib Sens Netw 11:1–13

    Google Scholar 

  15. Rodriguez A, Laio A (2014) Machine learning. Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

    Article  Google Scholar 

  16. Zhang TF, Ma FM (2016) Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function. Int J Comput Math 94(4):663–675. https://doi.org/10.1080/00207160.2015.1124099

    Article  MathSciNet  MATH  Google Scholar 

  17. Le OY, Dai DQ, Li XL, Wu M, Zhang XF, Yang P (2014) Detecting temporal protein complexes from dynamic protein–protein interaction networks. BMC Bioinform 15(1):1–14. https://doi.org/10.1186/1471-2105-15-335

    Article  Google Scholar 

  18. Zhang XX, Xiao QH, Li B, Hu S, Xiong HJ, Zhao BH (2015) Overlap maximum matching ratio (OMMR): a new measure to evaluate overlaps of essential modules. Front Inf Technol Electron Eng 16(4):293–300

    Article  Google Scholar 

  19. Xenarios I, Salwinski L, Duan XJ (2002) DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucl Acids Res 30(1):303–305

    Article  Google Scholar 

  20. Tu BP, Kudlicki A, Rowicka M, McKnight SL (2005) Logic of the yeast metabolic cycle: temporal compart mentalization of cellular processes. Science 310:1152–1158

    Article  Google Scholar 

  21. Pu S, Wong J, Turner B, Cho E, Wodak SJ (2009) Up-to-date catalogues of yeast protein complexes. Nucl Acids Res 37:825–831

    Article  Google Scholar 

  22. UniProt Consortium (2013) Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucl Acids Res 41(D1):D43–D47. https://doi.org/10.1093/nar/gks1068

    Article  Google Scholar 

  23. Wang JX, Peng XQ, Li M, Pan Y (2013) Construction and application of dynamic protein interaction network based on time course gene expression data. Proteomics 13(2):301–312

    Article  Google Scholar 

  24. Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform 4:2

    Article  Google Scholar 

  25. Nepusz T, Yu H, Paccanaro A (2012) Detecting overlapping protein complexes in protein–protein interaction networks. Nat Methods 9:471–472

    Article  Google Scholar 

  26. Li XL, Tan SH, Foo CS, Ng SK (2005) Interaction graph mining for protein complexes using local clique merging. Int Conf Genome Inf 16:260–269

    Google Scholar 

  27. Altafulamin M, Shinbo Y, Mihara K, Kurokawa K, Kanaya S (2006) Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinform 7:207

    Article  Google Scholar 

  28. Li M, Chen JE, Wang JX, Hu B, Chen G (2008) Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. BMC Bioinform 9:398

    Article  Google Scholar 

  29. Wang JX, Li M, Chen JE, Pan Y (2011) A fast hierarchical clustering algorithm for functional modules discovery in protein interaction networks. IEEE/ACM Trans Comput Biol Bioinf 8:607–620. https://doi.org/10.1109/TCBB.2010.75

    Article  Google Scholar 

  30. Shen XJ, Yi L, Jiang XP, Zhao YL, He TT, Yang JC (2015) Detecting temporal protein complexes based on neighbor closeness and time course protein interaction networks. IEEE Int Conf Bioinform Biomed. https://doi.org/10.1109/BIBM.2015.7359664

    Article  Google Scholar 

  31. Van Dongen S (2000) Graph clustering by flow simulation. Ph.D. Thesis. Utrecht: University of Utrecht

  32. Wu M, Li X, Kwoh CK, Ng SK (2009) A core-attachment based method to detect protein complexes in PPI networks. BMC Bioinform 10:169

    Article  Google Scholar 

  33. Shi J, Chen B, Wu FX (2012) Not all protein complexes exhibit dense structures in S. cerevisiae PPI network. In: IEEE international conference on bioinformatics and biomedicine, pp 470–473

  34. Samanta MP, Liang S (2003) Predicting protein functions from redundancies in large-scale protein interaction networks. PNAS 100:12579–12583

    Article  Google Scholar 

  35. Girvan M, Newman ME (2002) Community structure in social and biological networks. PNAS 99:7821–7826

    Article  MathSciNet  Google Scholar 

  36. Chatr-aryamontri A, Ceol A, Licata L, Cesareni G (2008) Protein interactions: integration leads to belief. Trends Biochem Sci 33:241–242

    Article  Google Scholar 

  37. Tan PP, Dargahi D, Pio F (2010) Predicting protein complexes by data integration of different types of interactions. Int J Comput Biol Drug Des 3:19–30

    Article  Google Scholar 

  38. Chen J, Yuan B (2006) Detecting functional modules in the yeast protein–protein interaction network. Bioinformatics 22:2283–2290

    Article  Google Scholar 

  39. Dittrich MT, Klau GW, Rosenwald A, Dandekar T, Müller T (2008) Identifying functional modules in protein–protein interaction networks: an integrated exact approach. Bioinformatics 24:i223–i231

    Article  Google Scholar 

  40. Li M, Wang JX, Chen JE (2008) A fast agglomerate algorithm for mining functional modules in protein interaction networks. In: 2008 International conference on biomedical engineering and informatics, vol 1, pp 3–7. (https://doi.org/10.1109/bmei.2008.121)

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Authors and Affiliations

  1. College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, China

    Dan Xie

  2. School of Computer, Central China Normal University, Wuhan, China

    Yang Yi & Jin Zhou

  3. Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China

    Xiaodong Li & Huikun Wu

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  1. Dan Xie
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  2. Yang Yi
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  3. Jin Zhou
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  4. Xiaodong Li
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  5. Huikun Wu
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Correspondence to Xiaodong Li.

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Xie, D., Yi, Y., Zhou, J. et al. A novel temporal protein complexes identification framework based on density–distance and heuristic algorithm. Neural Comput & Applic 31, 4693–4701 (2019). https://doi.org/10.1007/s00521-018-3660-5

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  • DOI: https://doi.org/10.1007/s00521-018-3660-5

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