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BFO-FMD: bacterial foraging optimization for functional module detection in protein–protein interaction networks

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Abstract

Identifying functional modules in PPI networks contributes greatly to the understanding of cellular functions and mechanisms. Recently, the swarm intelligence-based approaches have become effective ways for detecting functional modules in PPI networks. This paper presents a new computational approach based on bacterial foraging optimization for functional module detection in PPI networks (called BFO-FMD). In BFO-FMD, each bacterium represents a candidate module partition encoded as a directed graph, which is first initialized by a random-walk behavior according to the topological and functional information between protein nodes. Then, BFO-FMD utilizes four principal biological mechanisms, chemotaxis, conjugation, reproduction, and elimination and dispersal to search for better protein module partitions. To verify the performance of BFO-FMD, we compared it with several other typical methods on three common yeast datasets. The experimental results demonstrate the excellent performances of BFO-FMD in terms of various evaluation metrics. BFO-FMD achieves outstanding Recall, F-measure, and PPV while performing very well in terms of other metrics. Thus, it can accurately predict protein modules and help biologists to find some novel biological insights.

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References

  • Abdullah A, Deris S, Hashim SZM, Jamil HM (2009) Graph partitioning method for functional module detections of protein interaction network. In: Proceedings of the international conference on computer technology and development (ICCTD’09), pp 230–234

  • Adamcsek B, Palla G, Farkas IJ, Derényi I, Vicsek T (2006) CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22(8):1021–1023

    Article  Google Scholar 

  • Aldecoa R, Marín I (2010) Jerarca: efficient analysis of complex networks using hierarchical clustering. PLoS ONE 5(7):e11585

    Article  Google Scholar 

  • Aloy P, Böttcher B, Ceulemans H et al (2004) Structure-based assembly of protein complexes in yeast. Science 303(5666):2026–2029

    Article  Google Scholar 

  • Altaf-Ul-Amin 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(1):207

    Article  Google Scholar 

  • Arnau V, Mars S, Marín I (2005) Iterative cluster analysis of protein interaction data. Bioinformatics 21(3):364–378

    Article  Google Scholar 

  • Bader GD, Hogue CWV (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform 4(1):1

    Article  Google Scholar 

  • Balasubramaniam S, Lio P (2013) Multi-hop conjugation based bacteria nanonetworks. IEEE Trans Nanobiosci 12(1):47–59

    Article  Google Scholar 

  • Chin E, Zhu J (2013) B3Clustering: identifying protein complexes from protein–protein interaction network. In: Proceedings of Asia-Pacific web conference. Springer, Berlin, pp 108–119

  • Cho YR, Hwang W, Ramanathan M, Zhang A (2007) Semantic integration to identify overlapping functional modules in protein interaction networks. BMC Bioinform 8(1):265

    Article  Google Scholar 

  • Das S, Biswas A, Dasgupta S, Abrham A (2009) Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. Found Comput Intell 3:23–55

    Google Scholar 

  • Dwight SS, Harris MA, Dolinski K et al (2002) Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO). Nucleic Acids Res 30(1):69–72

    Article  Google Scholar 

  • Feng J, Jiang R, Jiang T (2011) A max-flow-based approach to the identification of protein complexes using protein interaction and microarray data. IEEE/ACM Trans Comput Biol Bioinform 8(3):621–634

    Article  Google Scholar 

  • Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976

    Article  MathSciNet  MATH  Google Scholar 

  • Friedel CC, Krumsiek J, Zimmer R (2008) Bootstrapping the interactome: unsupervised identification of protein complexes in yeast. In: Proceedings of annual international conference on research in computational molecular biology. Springer, Berlin, pp 3–16

  • Guimera R, Amaral LAN (2005) Functional cartography of complex metabolic networks. Nature 433(7028):895–900

    Article  Google Scholar 

  • Hinchey MG, Sterritt R, Rouff C (2007) Swarms and swarm intelligence. Computer 40(4):111–113

    Article  Google Scholar 

  • Inoue K, Li W, Kurata H (2010) Diffusion model based spectral clustering for protein–protein interaction networks. PLoS ONE 5(9):e12623

    Article  Google Scholar 

  • Ji J, Liu Z, Zhang A, Jiao L, Liu C (2012a) Improved ant colony optimization for detecting functional modules in protein–protein interaction networks. In: Proceedings of international conference on information computing and applications. Springer, Berlin, pp 404–413

  • Ji J, Liu Z, Zhang A, Jiao L, Liu C (2012b) Ant colony optimization with multi-agent evolution for detecting functional modules in protein–protein interaction networks. In: Proceedings of international conference on information computing and applications. Springer, Berlin, pp 445–453

  • Ji J, Liu Z, Zhang A, Yang C, Liu C (2013) HAM-FMD: mining functional modules in protein–protein interaction networks using ant colony optimization and multi-agent evolution. Neurocomputing 121:453–469

    Article  Google Scholar 

  • Ji J, Zhang A, Liu C, Quan X (2014a) Survey: functional module detection from protein–protein interaction networks. IEEE Trans Knowl Data Eng 26(2):261–277

    Article  Google Scholar 

  • Ji JZ, Liu ZJ, Liu HX, Liu CN (2014b) An overview of research on functional module Detection for protein–protein interaction networks. Acta Autom Sin 40(4):577–593

    Google Scholar 

  • Ji J, Liu H, Zhang A, Liu Z, Liu C (2015) ACC-FMD: ant colony clustering for functional module detection in protein–protein interaction networks. Int J Data Min Bioinform 11(3):331–363

    Article  Google Scholar 

  • King AD, Pržulj N, Jurisica I (2004) Protein complex prediction via cost-based clustering. Bioinformatics 20(17):3013–3020

    Article  Google Scholar 

  • Lei X, Wu S, Ge L, Zhang A (2011) Clustering PPI data based on bacteria foraging optimization algorithm. In: Proceedings of 2011 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 96–99

  • Lei X, Wu S, Ge L, Zhang A (2013) Clustering and overlapping modules detection in PPI network based on IBFO. Proteomics 13(2):278–290

    Article  Google Scholar 

  • Leung HCM, Xiang Q, Yiu SM, Chin FYL (2009) Predicting protein complexes from PPI data: a core-attachment approach. J Comput Biol 16(2):133–144

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  • Ma X, Gao L (2012) Predicting protein complexes in protein interaction networks using a core-attachment algorithm based on graph communicability. Inf Sci 189:233–254

    Article  Google Scholar 

  • Mete M, Tang F, Xu X, Yuruk N (2008) A structural approach for finding functional modules from large biological networks. BMC Bioinform 9(9):S19

    Article  Google Scholar 

  • Mewes HW, Amid C, Arnold R et al (2004) MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res 32(suppl 1):D41–D44

    Article  Google Scholar 

  • Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. Control Syst 22(3):52–67

    Article  Google Scholar 

  • Perales-Graván C, Lahoz-Beltra R (2008) An AM radio receiver designed with a genetic algorithm based on a bacterial conjugation genetic operator. IEEE Trans Evolut Comput 12(2):129–142

    Article  Google Scholar 

  • Qin G, Gao L (2010) Spectral clustering for detecting protein complexes in protein–protein interaction (PPI) networks. Math Comput Model 52(11):2066–2074

    Article  MathSciNet  MATH  Google Scholar 

  • Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551–1555

    Article  Google Scholar 

  • Sallim J, Abdullah R, Khader AT (2008) ACOPIN: an ACO algorithm with TSP approach for clustering proteins from protein interaction network. In: Proceedings of second UKSIM European symposium on computer modeling and simulation, pp 203–208

  • Schlicker A, Albrecht M (2008) FunSimMat: a comprehensive functional similarity database. Nucleic Acids Res 36(suppl 1):D434–D439

    Google Scholar 

  • Sen TZ, Kloczkowski A, Jernigan RL (2006) Functional clustering of yeast proteins from the protein–protein interaction network. BMC Bioinform 7(1):355

    Article  Google Scholar 

  • Tarassov K, Messier V, Landry CR, Radonovic S (2008) An in vivo map of the yeast protein interactome. Science 320(5882):1465–1470

    Article  Google Scholar 

  • Van Dongen S (2000) A cluster algorithm for graphs. Rep Inf Syst 10:1–40

    Article  Google Scholar 

  • Wu M, Li X, Kwoh CK, Ng SK (2009) A core-attachment based method to detect protein complexes in PPI networks. BMC Bioinform 10(1):169

    Article  Google Scholar 

  • Wu S, Lei X, Tian J (2011) Clustering PPI network based on functional flow model through artificial bee colony algorithm. In: Proceedings of 2011 seventh international conference on natural computation (ICNC’11), pp 92–96

  • Zhang A (2009) Protein interaction networks: computational analysis. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

Download references

Acknowledgements

This work was partly supported by the NSFC Research Program (61672065, 61375059) and the Beijing Municipal Education Research Plan Key Project (Beijing Municipal Fund Class B) (KZ201410005004).

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Authors and Affiliations

  1. Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Cuicui Yang & Junzhong Ji

  2. Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA

    Aidong Zhang

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  1. Cuicui Yang
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  2. Junzhong Ji
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  3. Aidong Zhang
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Correspondence to Junzhong Ji.

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Communicated by V. Loia.

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Yang, C., Ji, J. & Zhang, A. BFO-FMD: bacterial foraging optimization for functional module detection in protein–protein interaction networks. Soft Comput 22, 3395–3416 (2018). https://doi.org/10.1007/s00500-017-2584-9

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