A Bayesian Kernel for the Prediction of Protein- Protein Interactions

Authors: Hany Alashwal, Safaai Deris, Razib M. Othman

Abstract:

Understanding proteins functions is a major goal in the post-genomic era. Proteins usually work in context of other proteins and rarely function alone. Therefore, it is highly relevant to study the interaction partners of a protein in order to understand its function. Machine learning techniques have been widely applied to predict protein-protein interactions. Kernel functions play an important role for a successful machine learning technique. Choosing the appropriate kernel function can lead to a better accuracy in a binary classifier such as the support vector machines. In this paper, we describe a Bayesian kernel for the support vector machine to predict protein-protein interactions. The use of Bayesian kernel can improve the classifier performance by incorporating the probability characteristic of the available experimental protein-protein interactions data that were compiled from different sources. In addition, the probabilistic output from the Bayesian kernel can assist biologists to conduct more research on the highly predicted interactions. The results show that the accuracy of the classifier has been improved using the Bayesian kernel compared to the standard SVM kernels. These results imply that protein-protein interaction can be predicted using Bayesian kernel with better accuracy compared to the standard SVM kernels.

Keywords: Bioinformatics, Protein-protein interactions, Bayesian Kernel, Support Vector Machines.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330791

References:

[1] H. Lodish, A. Berk, L. Zipursky, P. Matsudaira, D. Baltimore, and J. Darnell, Molecular cell biology (4th edition). W.H. Freeman, New York, 2000.
[2] B. Alberts, A. Johnson, J. Lewis, M. Raff, K.Roberts, and P. Walter, Molecular Biology of the Cell (4th edition). Garland Science, 2002.
[3] T. Ito, K. Tashiro, S. Muta, R. Ozawa, T. Chiba, M. Nishizawa, K. Yamamoto, S. Kuhara, and Y. Sakaki, "Toward a protein-protein interaction map of the budding yeast: a comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins," Proc. Natl. Acad. Sci. USA. 97: 1143-1147, 2000.
[4] P. Uetz, L. Giot, G. Cagney, T.A. Mansfield, R.S. Judson, J.R. Knight, D. Lockshon, V. Narayan, M. Srinivasan, et al., "A Comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae," Nature 403:623 627, 2000.
[5] J. R. Newman, E. Wolf, and P. S. Kim, "A computationally directed screen identifying interacting coiled coils from Saccharomyces cerevisiae," Proc. Natl. Acad. Sci. U. S. A. 97, 13203-13208, 2000.
[6] P. Uetz and C. S. Vollert, "Protein-Protein Interactions," Encyclopedic Reference of Genomics and Proteomics in Molecular Medicine (ERGPMM), Springer Verlag, 2005.
[7] E. M. Phizicky and S. Fields, "Protein-protein interactions: Method for detection and analysis," Microbiological Reviews, pp.94-123, 1995.
[8] R. Jansen, H. Yu, D. Greenbaum, Y. Kluger, N.J. Krogan, S. Chung, A. Emili, M. Snyder, J.F. Greenblatt, and M. Gerstein. "A Bayesian networks approach for predicting protein-protein interactions from genomic data." Science. 302, pp:449-453, 2003.
[9] J. Yu, F. Fotouhi, and R.L. Finley. "Combining Bayesian Networks and Decision Trees to Predict Drosophila melanogaster Protein-Protein Interactions." In the 21st International Conference on Data Engineering Workshops. April 5-8. Tokyo, Japan. 2005.
[10] M. Sikora, F. Morcos, D.J. Costello, and J.A. Izaguirre. "Bayesian Inference of Protein and Domain Interactions Using the Sum-Product Algorithm." Proc. Information Theory and Applications Workshop, San Diego, Jan. 29, 2007.
[11] D. Koller. "Probabilistic Relational Models Source." Lecture Notes in Computer Science. 1634: 3-13. 1999.
[12] F. Fleuret and W. Gerstner. "A Bayesian Kernel for the Prediction of Neuron Properties from Binary Gene Profiles." Proceedings of the IEEE International Conference on Machine Learning and Applications. Special session Applications of Machine Learning in Medicine and Biology (ICMLA):129-134. 2005.
[13] D. Heckerman, D. Geiger and D.Chickering. "Learning Bayesian networks: The combination of knowledge and statistical data." Machine Learning. 20:197-243. 1995.
[14] P. Larra├▒aga, M.Y. Gallego, B. Sierra, L. Urkola, and M.J. Michelena. "Bayesian networks, rule induction and logistic regression in the prediction of the survival of women suffering from breast cancer." Lecture Notes in Artificial Intelligence. 1323. E. Costa, A. Cardoso (eds.):303-308. Springer-Verlag. 1997.
[15] M. Tipping. "The relevance vector machine." In Advances in Neural Information Processing Systems, 12:652-658. Cambridge MIT Press, 2000.
[16] D.S. Han, H.S. Kim, W.H. Jang, and S.D. Lee. "PreSPI: A Domain Combination Based Prediction System for Protein-Protein Interaction." Nucleic Acids Research. 32(21): 6312-6320. 2004.
[17] H. Alashwal, S. Deris, and R. M. Othman. "One-class support vector machines for protein-protein interactions prediction." International Journal of Biomedical Sciences, 1(2):120-127, 2006.
[18] C. C. Chang and C. J. Lin, "LIBSVM : a library for support vector machines," 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.