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Simultaneous Optimization of both Node and Edge Conservation in Network Alignment via WAVE

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Algorithms in Bioinformatics (WABI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9289))

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Abstract

Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Existing methods use a node cost function (NCF) to compare nodes across networks and an alignment strategy (AS) to find high-scoring alignments with respect to total NCF over all aligned nodes (or node conservation). Then, they evaluate alignments via a measure that is different than node conservation used to guide alignment construction. Typically, one measures edge conservation, but only after alignments are produced. Hence, we recently directly maximized edge conservation while constructing alignments, which improved their quality. Here, we aim to maximize both node and edge conservation during alignment construction to further improve quality. We design a novel measure of edge conservation that (unlike existing measures that treat each conserved edge the same) weighs conserved edges to favor edges with highly NCF-similar end-nodes. As a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can optimize any measures of node and edge conservation. Using WAVE on top of well-established NCFs improves alignments compared to existing methods that optimize only node or edge conservation or treat each conserved edge the same. We evaluate WAVE on biological data, but it is applicable in any domain.

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References

  1. 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)

    Article  Google Scholar 

  2. Milenković, T., Leong Ng, W., Hayes, W., Pržulj, N.: Optimal network alignment with graphlet degree vectors. Cancer Inform. 9, 121–137 (2010)

    Article  Google Scholar 

  3. Liao, C.-S., Kanghao, L., Baym, M., Singh, R., Berger, B.: IsoRankN: spectral methods for global alignment of multiple protein networks. Bioinformatics 25(12), i253–258 (2009)

    Article  Google Scholar 

  4. Kuchaiev, O., Pržulj, N.: Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 27(10), 1390–1396 (2011)

    Article  Google Scholar 

  5. Patro, R., Kingsford, C.: Global network alignment using multiscale spectral signatures. Bioinformatics 28(23), 3105–3114 (2012)

    Article  Google Scholar 

  6. Saraph, V., Milenković, T.: MAGNA: maximizing accuracy in global network alignment. Bioinformatics 30(20), 2931–2940 (2014)

    Article  Google Scholar 

  7. Faisal, F.E., Zhao, H., Milenković, T.: Global network alignment in the context of aging. IEEE/ACM Trans. Comput. Biol. Bioinf. 12(1), 40–52 (2014)

    Article  Google Scholar 

  8. Li, J., Tang, J., Li, Y., Luo, Q.: Rimom: a dynamic multistrategy ontology alignment framework. IEEE Trans. Knowl. Data Eng. 21(8), 1218–1232 (2009)

    Article  Google Scholar 

  9. Suchanek, F.M., Abiteboul, S., Senellart, P.: Paris: probabilistic alignment of relations, instances, and schema. Proc. VLDB Endowment 5(3), 157–168 (2011)

    Article  Google Scholar 

  10. Lacoste-Julien, S., Palla, K., Davies, A., Kasneci, G., Graepel, T., Ghahramani, Z.: Sigma: simple greedy matching for aligning large knowledge bases. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 572–580. ACM (2013)

    Google Scholar 

  11. Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: Proceedings of the 18th ICDE Conference (2002)

    Google Scholar 

  12. Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. J. Pattern Recognit Artif Intell. 18(03), 265–298 (2004)

    Article  Google Scholar 

  13. Zaslavskiy, M., Bach, Francis, Vert, J.-P.: A path following algorithm for the graph matching problem. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2227–2242 (2009)

    Article  Google Scholar 

  14. Koutra, D., Tong, H., Lubensky, D.: Big-align: fast bipartite graph alignment. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 389–398. IEEE (2013)

    Google Scholar 

  15. Zhang, Y., Tang, J.: Social network integration: towards constructing the social graph. CoRR, arXiv:abs/1311.2670 (2013)

  16. Bayati, M., Gerritsen, M., Gleich, D.F., Saberi, A., Wang, Y.: Algorithms for large, sparse network alignment problems. In: Ninth IEEE International Conference on Data Mining, ICDM 2009, pp. 705–710. IEEE (2009)

    Google Scholar 

  17. Torresani, L., Kolmogorov, V., Rother, C.: Feature correspondence via graph matching: models and global optimization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 596–609. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  18. Noma, A., Cesar, R.M.: Sparse representations for efficient shape matching. In: 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 186–192. IEEE (2010)

    Google Scholar 

  19. Duchenne, O., Bach, F., Kweon, I.-S., Ponce, J.: A tensor-based algorithm for high-order graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2383–2395 (2011)

    Article  Google Scholar 

  20. Smalter, A., Huan, J., Lushington, G.: Gpm: a graph pattern matching kernel with diffusion for chemical compound classification. In: 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008, pp. 1–6. IEEE (2008)

    Google Scholar 

  21. Faisal, F.E., Milenković, T.: Dynamic networks reveal key players in aging. Bioinformatics 30(12), 1721–1729 (2014)

    Article  Google Scholar 

  22. Cook, S.A.: The complexity of theorem-proving procedures. In: Proceedings of the 3rd Annual ACM Symposium on Theory of Computing, pp. 151–158 (1971)

    Google Scholar 

  23. Kelley, B.P., Yuan, B., Lewitter, F., Sharan, R., Stockwell, B.R., Ideker, T.: PathBLAST: a tool for alignment of protein interaction networks. Nucleic Acids Res. 32, 83–88 (2004)

    Article  Google Scholar 

  24. Sharan, R., Suthram, S., Kelley, R.M., Kuhn, T., McCuine, S., Uetz, P., Sittler, T., Karp, R.M., Ideker, T.: Conserved patterns of protein interaction in multiple species. Proc. Natl. Acad. Sci. 102(6), 1974–1979 (2005)

    Article  Google Scholar 

  25. Flannick, J., Novak, A., Srinivasan, B.S., McAdams, H.H., Batzoglou, S.: Graemlin general and robust alignment of multiple large interaction networks. Genome Res. 16(9), 1169–1181 (2006)

    Article  Google Scholar 

  26. Koyutürk, M., Kim, Y., Topkara, U., Subramaniam, S., Szpankowski, W., Grama, A.: Pairwise alignment of protein interaction networks. J. Comput. Biol. 13(2), 182–199 (2006)

    Article  MathSciNet  Google Scholar 

  27. Berg, J., Lässig, M.: Local graph alignment and motif search in biological networks. Proc. Natl. Acad. Sci. 101(41), 14689–14694 (2004)

    Article  Google Scholar 

  28. Liang, Z., Meng, X., Teng, M., Niu, L.: NetAlign: a web-based tool for comparison of protein interaction networks. Bioinformatics 22(17), 2175–2177 (2006)

    Article  Google Scholar 

  29. Berg, J., Lässig, M.: Cross-species analysis of biological networks by Bayesian alignment. Proc. Natl. Acad. Sci. 103(29), 10967–10972 (2006)

    Article  Google Scholar 

  30. Mina, M., Guzzi, P.H.: Improving the robustness of local network alignment: design and extensive assessment of a markov clustering-based approach. IEEE/ACM Trans. Comput. Biol. Bioinf. 99(PrePrints), 1 (2014)

    Google Scholar 

  31. Ciriello, G., Mina, M., Guzzi, P.H., Cannataro, M., Guerra, C.: AlignNemo: a local network alignment method to integrate homology and topology. PLOS ONE 7(6), e38107 (2012)

    Article  Google Scholar 

  32. Singh, R., Xu, J., Berger, B.: Pairwise global alignment of protein interaction networks by matching neighborhood topology. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS (LNBI), vol. 4453, pp. 16–31. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  33. Flannick, J.A., Novak, A.F., Do, C.B., Srinivasan, B.S., Batzoglou, S.: Automatic parameter learning for multiple network alignment. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS (LNBI), vol. 4955, pp. 214–231. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  34. Singh, R., Jinbo, X., Berger, B.: Global alignment of multiple protein interaction networks. Proc. Pac. Symp. Biocomputing 13, 303–314 (2008)

    Google Scholar 

  35. Zaslavskiy, M., Bach, F., Vert, J.-P.: Global alignment of protein-protein interaction networks by graph matching methods. Bioinformatics 25(12), i259–i267 (2009)

    Article  Google Scholar 

  36. Neyshabur, B., Khadem, A., Hashemifar, S., Arab, S.S.: NETAL: a new graph-based method for global alignment of protein-protein interaction networks. Bioinformatics 29(13), 1654–1662 (2013)

    Article  Google Scholar 

  37. Narayanan, A., Shi, E., IP Rubinstein, B.: Link prediction by de-anonymization: how we won the Kaggle social network challenge. In: Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1825–1834. IEEE (2011)

    Google Scholar 

  38. Guo, X., Hartemink, A.J.: Domain-oriented edge-based alignment of protein interaction networks. Bioinformatics 25(12), i240–1246 (2009)

    Article  Google Scholar 

  39. Klau, G.W.: A new graph-based method for pairwise global network alignment. BMC Bioinformatics 10(Suppl 1), S59 (2009)

    Article  Google Scholar 

  40. El-Kebir, M., Heringa, J., Klau, G.W.: Lagrangian relaxation applied to sparse global network alignment. In: Loog, M., Wessels, L., Reinders, M.J.T., de Ridder, D. (eds.) PRIB 2011. LNCS, vol. 7036, pp. 225–236. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  41. Clark, C., Kalita, J.: A comparison of algorithms for the pairwise alignment of biological networks. Bioinformatics, btu307 (2014)

    Google Scholar 

  42. Crawford, J., Sun, Y., Milenković, T.: Fair evaluation of global network aligners. Algorithms Mol. Biol. 10(19) (2015)

    Google Scholar 

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

    Google Scholar 

  44. Solava, R.W., Michaels, R.P., Milenković, T.: Graphlet-based edge clustering reveals pathogen-interacting proteins. Bioinformatics 18(28), i480–i486 (2012)

    Article  Google Scholar 

  45. Memišević, V., Milenković, T., Pržulj, N.: Complementarity of network and sequence information in homologous proteins. J. Integr. Bioinform. 7(3), 135 (2010)

    Google Scholar 

  46. Milenković, T., Memišević, V., Ganesan, A.K., Pržulj, N.: Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related interaction networks. J. R. Soc. Interface 7(44), 423–437 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  49. Hulovatyy, Y., Solava, R.W., Milenković, T.: Revealing missing parts of the interactome via link prediction. PLOS ONE 9(3), e90073 (2014)

    Article  Google Scholar 

  50. Milenković, T., Memišević, V., Bonato, A., Pržulj, N.: Dominating biological networks. PLOS ONE 6(8), e23016 (2011)

    Article  Google Scholar 

  51. Collins, S.R., Kemmeren, P., Zhao, X.-C., Greenblatt, J.F., Spencer, F., Holstege, F.C.P., Weissman, J.S., Krogan, N.J.: Toward a comprehensive atlas of the phyisical interactome of Saccharomyces cerevisiae. Molecular Cell. Proteomics 6(3), 439–450 (2007)

    Article  Google Scholar 

  52. Venkatesan, K., Rual, J.-F., Vazquez, A., Stelzl, U., Lemmens, I., Hirozane-Kishikawa, T., Hao, T., Zenkner, M., Xin, X., Goh, K.-I., et al.: An empirical framework for binary interactome mapping. Nat. Methods 6(1), 83–90 (2009)

    Article  Google Scholar 

  53. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)

    Article  Google Scholar 

  54. De Magalhães, J.P., Budovsky, A., Lehmann, G., Costa, J., Li, Y., Fraifeld, V., Church, G.M.: The human ageing genomic resources: online databases and tools for biogerontologists. Aging Cell 8(1), 65–72 (2009)

    Article  Google Scholar 

  55. Sharan, R., Ideker, T.: Modeling cellular machinery through biological network comparison. Nat. Biotechnol. 24(4), 427–433 (2006)

    Article  Google Scholar 

  56. Vijayan, V., Saraph, V., Milenković, T.: Magna++: maximizing accuracy in global network alignment via both node and edge conservation. Bioinformatics 31(14), 2409–2411 (2015)

    Article  Google Scholar 

  57. Faisal, F.E., Meng, L., Crawford, J., Milenković, T.: The post-genomic era of biological network alignment. EURASIP J. Bioinform. Syst. Biol. 2015(1), (2015)

    Google Scholar 

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Acknowledgements

This work was funded by the National Science Foundation CAREER CCF-1452795 and CCF-1319469 grants.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA

    Yihan Sun, Joseph Crawford & Tijana Milenković

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Yihan Sun & Jie Tang

  3. Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania

    Yihan Sun

Authors
  1. Yihan Sun
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  2. Joseph Crawford
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  4. Tijana Milenković
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Corresponding author

Correspondence to Joseph Crawford .

Editor information

Editors and Affiliations

  1. University of Maryland, College Park, Maryland, USA

    Mihai Pop

  2. University of Lille, Lille, France

    Hélène Touzet

Appendix

Appendix

1.1 Appendix Figures

Fig. A.1.
figure 10

Comparison of the edge-weighted and edge-unweighted versions of WAVE on topology-only alignments of real-world PPI networks of different species with respect to (a) S\(^3\), (b) LCCS, and (c) Exp-GO.

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Fig. A.2.
figure 11

Comparison of the edge-weighted and edge-unweighted versions of WAVE on best alignments of real-world PPI networks of different species with respect to (a) S\(^3\), (b) LCCS, and (c) Exp-GO.

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Fig. A.3.
figure 12

Remaining results for overall ranking of each method over all network pairs in a given data set and over all alignment quality measures. The ranking is expressed as a percentage of all cases in which the given method ranks as the \(k^{th}\) best method. That is, the more cases in which a given method achieves a higher ranking, the better the method. For example, in panel (b), M-W is the highest scoring of all methods shown on x-axis, since it is ranked the highest (i.e., as the \(1^{st}\) best method) in most of the cases. (a) Results for the five NCF-AS methods on best alignments of “synthetic” (noisy yeast) networks. (b) Results for the five NCF-AS methods on best alignments of real-world PPI networks of different species. Details (per network pair and alignment quality measure) for panels (a)-(b) are shown in Figs. A.4 and A.5. Recall that M-M and G-G are MI-GRAAL and GHOST.

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Fig. A.4.
figure 13

Comparison of the five NCF-AS methods on best alignments of “synthetic” (noisy yeast) networks with respect to: (a) NC, (b) S\(^3\), (c) LCCS, and (d) Exp-GO.

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Fig. A.5.
figure 14

Comparison of the five NCF-AS methods on best alignments of real-world PPI networks of different species with respect to: (a) S\(^3\), (b) LCCS, and (c) Exp-GO.

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Fig. A.6.
figure 15

Comparison of WAVE (the best of M-W and G-W) with very recent network alignment methods on topology-only alignments of “synthetic” (noisy yeast) networks with respect to: (a) NC, (b) S\(^3\), (c) LCCS, and (d) Exp-GO.

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Fig. A.7.
figure 16

Comparison of WAVE (the best of M-W and G-W) with very recent network alignment methods on topology-only alignments of real-world PPI networks of different species with respect to: (a) S\(^3\), (b) LCCS, and (c) Exp-GO.

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Sun, Y., Crawford, J., Tang, J., Milenković, T. (2015). Simultaneous Optimization of both Node and Edge Conservation in Network Alignment via WAVE. In: Pop, M., Touzet, H. (eds) Algorithms in Bioinformatics. WABI 2015. Lecture Notes in Computer Science(), vol 9289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48221-6_2

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  • DOI: https://doi.org/10.1007/978-3-662-48221-6_2

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