A Bayesian framework for combining protein and network topology information for predicting protein-protein interactions

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 12, Issue 3, 2015, Pages 538-550

  Birlutiu, Adriana ^a,b   D'Alché Buc, Florence ^c   Heskes, Tom ^a  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

^b University Alba   (Romania)

^c UNIVERSITÉ D EVRY VAL D ESSONNE   (France)

Author keywords

Bayesian methods; Network analysis; Protein protein interaction; Topology

Indexed keywords

BAYESIAN NETWORKS; COMPUTATIONAL METHODS; ELECTRIC NETWORK ANALYSIS; ELECTRIC NETWORK TOPOLOGY; FORECASTING; INFERENCE ENGINES; LABORATORIES; TOPOLOGY;

BAYESIAN FRAMEWORKS; BAYESIAN INFERENCE; BAYESIAN METHODS; HIGH THROUGHPUT TECHNOLOGY; LABORATORY EXPERIMENTS; NETWORK TOPOLOGY; PROTEIN-PROTEIN INTERACTIONS; TOPOLOGICAL PROPERTIES;

PROTEINS;

FUNGAL PROTEIN; PROTEIN;

ALGORITHM; BAYES THEOREM; BIOLOGY; CHEMISTRY; FACTUAL DATABASE; HUMAN; PROCEDURES; PROTEIN ANALYSIS;

ALGORITHMS; BAYES THEOREM; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; FUNGAL PROTEINS; HUMANS; PROTEIN INTERACTION MAPPING; PROTEINS;

EID: 84940372176     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2014.2359441     Document Type: Article

References (67)

1. A. Barabasi, N. Gulbahce, and J. Loscalzo, "Network medicine: A network-based approach to human disease." Nat. Reviews. Genetics, Vol. 12, no. 1, pp. 56-68, 2011.
2. N. Friedman, "Inferring cellular networks using probabilistic graphical models," Science, Vol. 303, no. 5659, pp. 799-805, 2004.
3. R. Jansen, H. Yu, D. Greenbaum, Y. Kluger, N. Krogan, S. Chung, A. Emili, M. Snyder, J. Greenblatt, and M. Gerstein, "A Bayesian networks approach for predicting protein-protein interactions from genomic data," Science, Vol. 302, no. 5644, pp. 449-453, 2003.
4. A. Ben-Hur and W. S. Noble, "Kernel methods for predicting protein-protein interactions," Bioinformatics, Vol. 21, no. 1, pp. 38-46, 2005.
5. Y. Yamanishi, J.-P. Vert, and M. Kanehisa, "Protein network inference from multiple genomic data: a supervised approach," Bioinformatics, Vol. 20, no. 1, pp. 363-370, 2004.
6. N. Lin, B. Wu, R. Jansen, M. Gerstein, and H. Zhao, "Information assessment on predicting protein-protein interactions." BMC Bioinformatics, Vol. 5, p. 154, 2004.
7. E. Sprinzak Y. Altuvia, and H. Margalit, "Characterization and prediction of proteinprotein interactions within and between complexes," PNAS, Vol. 103, no. 40, p. 14718-14723, 2006.
8. L. V. Zhang, S. L. Wong, O. D. King, and F. P. Roth, "Predicting co-complexed protein pairs using genomic and proteomic data integration." BMC Bioinformatics, Vol. 5, p. 38, 2004.
9. Y. Qi, J. Klein-Seetharaman, and Z. Bar-Joseph, "Random forest similarity for protein-protein interaction prediction from multiple sources." in Pacific Symposium on Biocomputing, R. B. Altman, T. A. Jung, T. E. Klein, A. K. Dunker, and L. Hunter, Eds. Singapore: World Scientific, 2005.
10. X.-W. Chen and M. Liu, "Prediction of protein-protein interactions using random decision forest framework." Bioinformatics, Vol. 21, no. 24, pp. 4394-4400, 2005.
11. P. Geurts, L. Wehenkel, and d'Alche Buc F., "Kernelizing the output of tree-based methods," in Proc. 23th Int. Conf. Mach. Learn., 2006, pp. 345-352.
12. P. Geurts, N. Touleimat, M. Dutreix, and F. d'Alche-Buc, "Inferring biological networks with output kernel trees," BMC Bioinformatics (PMSB06 special issue), Vol. 8, no. Suppl 2, p. S4, 2007.
13. C. Brouard, F. d'Alche Buc, and M. Szafranski, "Semi-supervised penalized output kernel regression for link prediction," in Proc. Int. Conf. Mach. Learning, 2011, pp. 593-600.
14. C. Xing and D. B. Dunson, "Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions," PLoS Comput. Biol., Vol. 7, no. 7, p. e1002110, 2011.
15. H. Jeong, S. Mason, A.-L. Barabasi, and Z. Oltvai, "Lethality and centrality in protein networks," Nature, Vol. 411, no. 6833, pp. 41-42, 2001.
16. S. Maslov and K. Sneppen, "Specificity and stability in topology of protein networks," Science, Vol. 296, pp. 910-913, 2002.
17. C. C. Friedel and R. Zimmer, "Inferring topology from clustering coefficients in protein-protein interaction networks." BMC Bioinformatics, Vol. 7, p. 519,2006.
18. N. Przulj, D. G Corneil, and I. Jurisica, "Modeling interactome: scale-free or geometric?." Bioinformatics, Vol. 20, no. 18, pp. 3508-3515, 2004.
19. A. Sarajli, V. Janji, N. Stojkovi, D. Radak, and N. Prulj, "Network topology reveals key cardiovascular disease genes," PLoS ONE, Vol. 8, no. 8, p. e71537, 08 2013.
20. Z.-C. Li, Y.-H. Lai, L.-L. Chen, Y. Xie, Z. Dai, and X.-Y. Zou, "Identifying functions of protein complexes based on topology similarity with random forest," Mol Biosyst, no. 10, pp. 514-525, 2014.
21. A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener, "Graph structure in the web," Comput. Netw., Vol. 33, no. 1-6, pp. 309-320, 2000.
22. S. Redner, "How popular is your paper? an empirical study of the citation distribution," Eur. Phys. J. B, Vol. 4, pp. 131-134, 1998.
23. C. Lippert, O. Stegle, Z. Ghahramani, and K. Borgwardt, "A kernel method for unsupervised structured network inference," in Proc. 12th Int. Conf. Artif. Intell. Stat., 2009, pp. 368-375.
24. P. Symeonidis, N. Iakovidou, N. Mantas, and Y. Manolopoulos, "From biological to social networks: Link prediction based on multi-way spectral clustering," Data Knowl. Eng., Vol. 87, pp. 226-242, 2013.
25. P. Sheridan, T. Kamimura, and H. Shimodaira, "A scale-free structure prior for graphical models with applications in functional genomics," PLoS ONE, Vol. 5, no. 11, pp. e13 580+, 2010.
26. C. Wiuf, M. Brameier, O. Hagberg, and M. P. H. Stumpf, "A likelihood approach to analysis of network data," Proc. Nat. Acad. Sci., Vol. 103, no. 20, pp. 7566-7570, 2006.
27. M. Fiori, P. Muse, and G Sapiro, "Topology constraints in graphical models," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 800-808.
28. Q. Liu and A. Ihler, "Learning scale free networks by reweighted l1 regularization," in Proc. 14th Int. Conf. Artif. Intell. Stat., 2011, pp. 40-48.
29. R. Tandon and P. Ravikumar, "Learning graphs with a few hubs," in Proc. 31th Int. Conf. Mach. Learn., 2014, pp. 602-610.
30. A. Birlutiu and T. Heskes, "Using topology information for protein-protein interaction prediction," in Pattern Recognition in Bioin-formatics, ser. Lecture Notes in Computer Science, M. Comin, L. Kll, E. Marchiori, A. Ngom, and J. Rajapakse, Eds. New York, NY, USA: Springer, 2014, vol. 8626, pp. 10-22.
31. M. A. Yildirim, K.-I. Goh, M. E. Cusick, A.-L. Barabasi, and M. Vidal, "Drug-target network," Nat. Biotech., vol. 25, no. 10, pp. 1119-1126, Oct. 2007.
32. T. Milenkovic and N. Przulj, "Uncovering biological network function via graphlet degree signatures," Cancer Informatics, vol. 6, pp. 257-273, 2008.
33. O. Kuchaiev, M. Rasajski, D. J. Higham, and N. Przulj, "Geometric de-noising of protein-protein interaction networks," PLOS Comput. Biol., vol. 5, no. 8, 2009.
34. C. Lei and J. Ruan, "A novel link prediction algorithm for reconstructing protein-protein interaction networks by topological similarity," Bioinformatics, vol. 29, no. 3, pp. 355-364, 2013.
35. V. Memisevic, T. Milenkovic, and N. Przulj, "Complementarity of network and sequence information in homologous proteins," J. Integr. Bioinformatics, vol. 7, no. 3, pp. 135, 2010.
36. T. Kato, K. Tsuda, and K. Asai. (2005, May). Selective integration of multiple biological data for supervised network inference. Bio-informatics, vol. 21, no. 10, pp. 2488-2495. [Online]. Available: http://dx.doi.org/10.1093/bioinformatics/bti339
37. Y. Qi, J. Klein-Seetharaman, and Z. Bar-Joseph, "A mixture of feature experts approach for protein-protein interaction prediction," BMC Bioinformatics, vol. 8, (Suppl 10):S6, 2007.
38. J.-P. Vert and Y. Yamanishi. (2005). Supervised graph inference. in Advances in Neural Information Processing Systems., Cambridge, MA, USA: MIT Press, pp. 1433-1440. [Online]. Available: http://eprints.pascal-network.org/archive/00001405/
39. Y. Yamanishi, J.-P. Vert, and M. Kanehisa. (2005, Jan.) Supervised enzyme network inference from the integration of genomic data and chemical information. Bioinformatics, vol. 21, no. 1, pp. 468-477. [Online]. Available: http://dx.doi.org/10.1093/bioinformat-ics/bti1012
40. N. Zaki, S. Lazarova-Molnar, W. El-Hajj, and P. Campbell, "Protein-protein interaction based on pairwise similarity." BMC Bioinformatics, vol. 10, p. 150, 2009.
41. T. F. Smith and M. S. Waterman. (1981). Identification of common molecular subsequences. J. Mol. Biol., vol. 147, no. 1, pp. 195-197. [Online]. Available: http://view.ncbi.nlm.nih.gov/pubmed/7265238
42. M. Hue and J.-P. Vert, "On learning with kernels for unordered pairs," in Proc. Int. Conf. Mach. Learning, 2010, pp. 463-470.
43. T. P. Mohamed, J. G. Carbonell, and M. K. Ganapathiraju, "Active learning for human protein-protein interaction prediction," BMC Bioinformatics, vol. 11(Suppl 1):S57, 2010.
44. Y. Qi, O. Tastan, J. Carbonell, J. Klein-Seetharaman, and J. Weston, "Semi-supervised multi-task learning for predicting interactions between hiv-1 and human proteins," Bioinformatics, vol. 26, no. 18, pp. i645-i652, 2010.
45. H. Kashima, Y. Yamanishi, T. Kato, M. Sugiyama, and K. Tsuda, "Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information," Bio-informatics, vol. 25, no. 22, pp. 2962-2968, 2009.
46. C.-Y. Yu, L.-C. Chou, and D. T.-H. Chang, "Predicting protein-protein interactions in unbalanced data using the primary structure of proteins," BMC Bioinformatics, vol. 11, no. 167, 2010.
47. M. Hue, M. Riffle, J. Vert, and W. Noble, "Large-scale prediction of protein-protein interactions from structures," BMC Bioinformatics, vol. 11, no. 144, 2010.
48. D. Ekman, S. Light, A. K. Bjorklund, and A. Elofsson, "What properties characterize the hub proteins of the protein-protein interaction network of saccharomyces cerevisiae?" Genome Biol., vol. 7, no. R45, 2006.
49. D. J. Watts and S. H. Strogatz, "Collective dynamics of /'small-world/' networks," Nature, vol. 393, no. 6684, pp. 440-442, 1998.
50. F. Chung and L. Lu, "The average distances in random graphs with given expected degrees," Internet Math., vol. 1, pp. 15 879-15 882, 2002.
51. F. Chung and L. Lu, "Connected components in random graphs with given expected degree sequences," Ann. Combinatorics, Vol. 6, no. 2, pp. 125-145, 2002.
52. A. L. Barabasi and R. Albert, "Emergence of scaling in random networks," Science (New York, N.Y.), Vol. 286, no. 5439, pp. 509-512, 1999.
53. D. Pennock, G. W. Flake, S. Lawrence, E. J. Glover, and C. L. Giles, "Winners don't take all: Characterizing the competition for links on the web," in Proc. Nat. Acad. Sci., 2002, pp. 5207-5211.
54. M. Mitzenmacher, "A brief history of generative models for power law and lognormal distributions." Internet Math., Vol. 1, no. 2, 2003.
55. A. Todor, A. Dobra, and T. Kahveci, "Characterizing the topology of probabilistic biological networks," IEEE/ACM Trans. Comput. Biol. Bioinformatics, Vol. 10, no. 4, pp. 970-983, Dec. 2013.
56. O. Tastan, Y. Qi, J. Carbonell, and J. Klein-Seetharaman, "Prediction of interactions between hiv-1 and human proteins by information integration," Proc. Pac. Symp. Biocomput., Vol. 14, pp. 516-527, 2009.
57. C. von Mering, R. Krause, B. Snel, M. Cornell, S. Oliver, S. Fields, and P. Bork, "Comparative assessment of large-scale datasets of protein-protein interactions," Nature, Vol. 417, no. 6887, pp. 399-403, 2002.
58. J. Ma, S. Wang, Z. Wang, and J. Xu, "MRFalign: Protein homology detection through alignment of markov random fields," PLoS Comput Biol, Vol. 10, no. 3, p. e1003500, 03 2014.
59. J. Soding, "Protein homology detection by HMM-HMM comparison," Bioinformatics, Vol. 21, no. 7, pp. 951-960, Apr. 2005.
60. M. Deng, S. Mehta, F. Sun, and C. T., "Inferring domain-domain interactions from protein-protein interactions," Genome Res, Vol. 12, no. 10, pp. 1540-8, 2002.
61. J. Quinlan, C4.5: Programs for Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers, 1993.
62. L. Breiman, "Random forests," Mach. Learn., Vol. 45, no. 1, pp. 5-32, Oct. 2001.
63. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. (2009, Nov.). The weka data mining software: An update. SIGKDD Explor. Newsl., Vol. 11, no. 1, pp. 10-18. [Online]. Available: http://doi.acm.org/10.1145/1656274.1656278
64. Y. Qi, Z. Bar-Joseph, and J. Klein-Seetharaman, "Evaluation of different biological data and computational classification methods for use in protein interaction prediction," Proteins: Struct., Funct., Bioinform., Vol. 63, no. 3, pp. 490-500, 2006.
65. R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, "Network motifs: Simple building blocks of complex networks," Science, Vol. 298, no. 5594, pp. 824-827, 2002.
66. ∗) models for social networks," Soc. Netw., Vol. 29, no. 2, pp. 173-191, May 2007
67. R. Tanaka, T.-M. Yi, and J. Doyle, "Some protein interaction data do not exhibit power law statistics," FEBS Letters, Vol. 579, pp. 5140-5144,2005.