Skip to main content
SpringerLink
Log in

Comparing Different Graphlet Measures for Evaluating Network Model Fits to BioGRID PPI Networks

  • Conference paper
  • First Online:
Algorithms for Computational Biology (AlCoB 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11488))

Included in the following conference series:

Abstract

The network structure of protein-protein interaction (PPI) networks has been studied for over a decade. Many theoretical models have been proposed to model PPI networks, but continuing noise and incompleteness in these networks make conclusions difficult. Graphlet-based measures are believed to be among the strongest, most discerning and sensitive network comparison tools available. Several graphlet-based measures have been proposed to measure topological agreement between networks and models, with little work done to compare the measures themselves. The last modeling attempt was 4 years ago; it is time for an update. Using Sept. 2018 BioGRID, we fit eight theoretical models to nine BioGRID networks using four different graphlet-based measures. We find the following: (1) Graph Kernel is the best measure based on ROC and AUPR curves; (2) most graphlet measures disagree on the ordering of the data-model fits, although most agree on the top two (STICKY and Hyperbolic Geometric) and bottom two (ER and GEO) models, in direct contradiction to the 4-years-ago conclusion that GEO models are best; (3) the STICKY model is overall the best fit for these PPI networks but the Hyperbolic Geometric model is a better fit than STICKY on 4 species; and (4) even the best models provide p-values for BioGRID that are many orders of magnitude smaller than 1, thus failing any reasonable hypothesis test. We conclude that in spite of STICKY being the best fit, all BioGRID networks fail all hypothesis tests against all existing models, using all existing graphlet-based measures. Further work is needed to discover whether the data or the models are at fault.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aldecoa, R., Orsini, C., Krioukov, D.: Hyperbolic graph generator. Comput. Phys. Commun. 196, 492–496 (2015)

    Article  Google Scholar 

  2. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  3. Barabási, A., Dezso, Z., Ravasz, E., Yook, Z.H., Oltvai, Z.N.: Scale-free and hierarchical structures in complex networks. In: Modeling of Complex Systems: Seventh Granada Lectures. AIP Conference Proceedings, vol. 661, pp. 1–16 (2003)

    Google Scholar 

  4. Bianconi, G., Pin, P., Marsili, M.: Assessing the relevance of node features for network structure. Proc. Nat. Acad. Sci. 106(28), 11433–11438 (2009)

    Article  Google Scholar 

  5. Chatr-Aryamontri, A., et al.: The BioGRID interaction database: 2017 update. Nucleic Acids Res. 45(D1), D369–D379 (2017)

    Article  Google Scholar 

  6. Davis, D., Yaveroğlu, Ö.N., Malod-Dognin, N., Stojmirovic, A., Pržulj, N.: Topology-function conservation in protein-protein interaction networks. Bioinformatics 31(10), 1632–1639 (2015). https://doi.org/10.1093/bioinformatics/btv026

    Article  Google Scholar 

  7. Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240. ACM (2006)

    Google Scholar 

  8. Erdös, P., Rényi, A.: On random graphs. Publicationes Mathematicae 6, 290–297 (1959)

    MathSciNet  MATH  Google Scholar 

  9. Hayes, W., Sun, K., Pržulj, N.: Graphlet-based measures are suitable for biological network comparison. Bioinformatics 29(4), 483–491 (2013)

    Article  Google Scholar 

  10. Higham, D., Rašajski, M., Pržulj, N.: Fitting a geometric graph to a protein-protein interaction network. Bioinformatics 24(8), 1093–1099 (2008)

    Article  Google Scholar 

  11. Hočevar, T., Demšar, J.: A combinatorial approach to graphlet counting. Bioinformatics 30(4), 559–565 (2014). https://doi.org/10.1093/bioinformatics/btt717

    Article  Google Scholar 

  12. Janjić, V., Pržulj, N.: The topology of the growing human interactome data. J. Integr. Bioinform. 11(2), 27–42 (2014)

    Google Scholar 

  13. Janjić, V., Sharan, R., Pržulj, N.: Modelling the yeast interactome. Sci. Rep. 4, 4273 (2014)

    Article  Google Scholar 

  14. Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9(10), 770 (2008)

    Article  Google Scholar 

  15. Kashtan, N., Itzkovitz, S., Milo, R., Alon, U.: Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11), 1746–1758 (2004)

    Article  Google Scholar 

  16. Kotlyar, M., Pastrello, C., Malik, Z., Jurisica, I.: IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species. Nucleic Acids Res. 47(D1), D581–D589 (2018)

    Article  Google Scholar 

  17. Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., Boguná, M.: Hyperbolic geometry of complex networks. Phys. Rev. E 82(3), 036106 (2010)

    Article  MathSciNet  Google Scholar 

  18. Kuchaiev, O., Pržulj, N.: Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 27, 1390–1396 (2011). https://doi.org/10.1093/bioinformatics/btr127

    Article  Google Scholar 

  19. Kuchaiev, O., Milenković, T., Memišević, V., Hayes, W., Pržulj, N.: Topological network alignment uncovers biological function and phylogeny. J. R. Soc. Interface 7(50), 1341–1354 (2010). https://doi.org/10.1098/rsif.2010.0063

    Article  Google Scholar 

  20. Luck, K., Sheynkman, G.M., Zhang, I., Vidal, M.: Proteome-scale human interactomics. Trends Biochem. Sci. 42, 342–354 (2017)

    Article  Google Scholar 

  21. Luo, F., Yang, Y., Chen, C.F., Chang, R., Zhou, J., Scheuermann, R.H.: Modular organization of protein interaction networks. Bioinformatics 23(2), 207–214 (2006)

    Article  Google Scholar 

  22. Malod-Dognin, N., Pržulj, N.: L-GRAAL: Lagrangian graphlet-based network aligner. Bioinformatics 31(13), 2182–2189 (2015)

    Article  Google Scholar 

  23. Mamano, N., Hayes, W.B.: SANA: simulated annealing far outperforms many other search algorithms for biological network alignment. Bioinformatics 33, 2156–2164 (2017)

    Article  Google Scholar 

  24. Milano, M., et al.: An extensive assessment of network alignment algorithms for comparison of brain connectomes. BMC Bioinform. 18(6), 235 (2017)

    Article  Google Scholar 

  25. Milenković, T., Lai, J., Pržulj, N.: GraphCrunch: a tool for large network analyses. BMC Bioinform. 9, 70 (2008)

    Article  Google Scholar 

  26. Milenković, T., Pržulj, N.: Uncovering biological network function via graphlet degree signatures. Cancer Inform. 6, 257–273 (2008)

    Article  Google Scholar 

  27. Milenković, T., Ng, W.L., Hayes, W., Pržulj, N.: Optimal network alignment with graphlet degree vectors. Cancer Inform. 9, 121–137 (2010). https://doi.org/10.4137/CIN.S4744. http://www.la-press.com/optimal-network-alignment-with-graphlet-degree-vectors-article-a2141

    Article  Google Scholar 

  28. Penrose, M.: Random Geometric Graphs. Oxford Studies in Probability. Oxford University Press, Oxford (2003)

    Book  Google Scholar 

  29. Petit, J., Kavelaars, J., Gladman, B., Loredo, T.: Size distribution of multikilometer transneptunian objects. In: The Solar System Beyond Neptune, pp. 71–87 (2008)

    Google Scholar 

  30. Pinkert, S., Schultz, J., Reichardt, J.: Protein interaction networks-more than mere modules. PLoS Comput. Biol. 6(1), e1000659 (2010)

    Article  MathSciNet  Google Scholar 

  31. Pržulj, N.: Biological network comparison using graphlet degree distribution. Bioinformatics 20, e177–e183 (2007)

    Article  Google Scholar 

  32. Pržulj, N., Corneil, D.G., Jurisica, I.: Modeling interactome: scale-free or geometric? Bioinformatics 20(18), 3508–3515 (2004). https://doi.org/10.1093/bioinformatics/bth436. http://bioinformatics.oxfordjournals.org/content/20/18/3508.abstract

    Article  Google Scholar 

  33. Pržulj, N., Higham, D.: Modelling protein-protein interaction networks via a stickiness index. J. R. Soc. Interface 3(10), 711–716 (2006)

    Article  Google Scholar 

  34. Pržulj, N., Kuchaiev, O., Stevanović, A., Hayes, W.: Geometric evolutionary dynamics of protein interaction networks. In: Pacific Symposium on Biocomputing (2010)

    Google Scholar 

  35. Pržulj, N., Kuchaiev, O., Stevanović, A., Hayes, W.: Geometric evolutionary dynamics of protein interaction networks. In: Proceedings of the 2010 Pacific Symposium on Biocomputing (PSB), 4–8 January 2010, Big Island, Hawaii (2010)

    Google Scholar 

  36. Pržulj, N., Milenković, T.: Computational methods for analyzing and modeling biological networks. In: Chen, J., Lonardi, S. (eds.) Biological Data Mining. CRC Press (2009, To appear)

    Google Scholar 

  37. Pržulj, N.: Biological network comparison using graphlet degree distribution. Bioinformatics 23(2), e177–e183 (2007)

    Article  Google Scholar 

  38. Rito, T., Wang, Z., Deane, C.M., Reinert, G.: How threshold behaviour affects the use of subgraphs for network comparison. Bioinformatics 26(18), i611–i617 (2010). https://doi.org/10.1093/bioinformatics/btq386

    Article  Google Scholar 

  39. Salathé, M., May, R.M., Bonhoeffer, S.: The evolution of network topology by selective removal. R. Soc. Interface 2, 533–536 (2005)

    Article  Google Scholar 

  40. Shen-Orr, S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31(1), 64–68 (2002)

    Article  Google Scholar 

  41. Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., Borgwardt, K.: Efficient graphlet kernels for large graph comparison. In: Artificial Intelligence and Statistics, pp. 488–495 (2009)

    Google Scholar 

  42. Sole, R.V., Manrubia, S.C., Benton, M., Bak, P.: Self-similarity of extinction statistics in the fossil record. Nature 388(6644), 764 (1997)

    Article  Google Scholar 

  43. Vázquez, A., Flammini, A., Maritan, A., Vespignani, A.: Modeling of protein interaction networks. Complexus 1(1), 38–44 (2003)

    Article  Google Scholar 

  44. Vijayan, V., Saraph, V., Milenković, T.: MAGNA++: maximizing accuracy in global network alignment via both node and edge conservation. Bioinformatics 31, 2409–2411 (2015)

    Article  Google Scholar 

  45. Vishveshwara, S., Brinda, K., Kannan, N.: Protein structure: insights from graph theory. J. Theor. Comput. Chem. 1(01), 187–211 (2002)

    Article  Google Scholar 

  46. Wang, Z., Zhang, J.: In search of the biological significance of modular structures in protein networks. PLoS Comput. Biol. 3(6), e107 (2007)

    Article  MathSciNet  Google Scholar 

  47. Yaveroğlu, N., et al.: Revealing the hidden language of complex networks. Sci. Rep. 4, 4547 (2014)

    Article  Google Scholar 

  48. Yaveroğlu, Ö.N., Milenković, T., Pržulj, N.: Proper evaluation of alignment-free network comparison methods. Bioinformatics 31(16), 2697–2704 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of California, Irvine, Irvine, USA

    Sridevi Maharaj, Zarin Ohiba & Wayne Hayes

Authors
  1. Sridevi Maharaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zarin Ohiba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wayne Hayes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sridevi Maharaj .

Editor information

Editors and Affiliations

  1. University of California, Berkeley, Berkeley, CA, USA

    Ian Holmes

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. University of Extremadura, Cáceres, Spain

    Miguel A. Vega-Rodríguez

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Maharaj, S., Ohiba, Z., Hayes, W. (2019). Comparing Different Graphlet Measures for Evaluating Network Model Fits to BioGRID PPI Networks. In: Holmes, I., Martín-Vide, C., Vega-Rodríguez, M. (eds) Algorithms for Computational Biology. AlCoB 2019. Lecture Notes in Computer Science(), vol 11488. Springer, Cham. https://doi.org/10.1007/978-3-030-18174-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-18174-1_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18173-4

  • Online ISBN: 978-3-030-18174-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation