Identification of Important Proteins in Protein Interaction Network Based on SVM

Abstract:

Importance of proteins are different to perform functions of cells in living organisms according to the relevant experiment results, and more essential proteins is the most important kind of proteins. There are recently many computational approaches proposed to predict essential proteins in network level through network topologies combined with biological information of proteins. However it is still hard to identify them because of limitations of topological centralities and bioinformatic sources. And more it is the challenge is to perform better with less resources. Therefore in this paper, we first examine the correlation between common topological centralities and essential proteins and choose a few particular centralities, and then to build a SVM model, names as TC-SVM, for predicting the essential proteins. The new method has been applied to a yeast protein interaction networks, which are obtained from the BioGRID database. The ten folds experimental results show that the performance of predicting essential proteins by TC-SVM is excellent.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 5202-5206
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.5202
• Online since: September 2014
• Authors: Yan Li Zha, Wan Cheng Luo*
• Keywords: Essential Protein, Protein Interaction Network, Support Vector Machine (SVM), Topological Feature

References

[1] Jeong, H., Mason, S. P., Barabasi, A. L., and Oltvai, Z. N. Lethality and centrality in protein networks, Nature, 411(6833), pp.41-42, (2001).

DOI: 10.1038/35075138

[2] Giaever, G., Chu, A. M., Ni, L., Connelly, etc. Functional profiling of the Saccharomyces cerevisiae genome, Nature, 418(6896), pp.387-391, (2002).

[3] Joy, M. P., Brock, A., Ingber, D. E., and Huang, S. High-Betweenness Proteins in the Yeast Protein Interaction Network, Journal of Biomedicine and Biotechnology, (2005).

DOI: 10.1155/jbb.2005.96

[4] Watts, D. J., and Strogatz, S. H. Collective dynamics of 'small-world' networks, Nature, 393(6684), pp.440-442, (1998).

DOI: 10.1038/30918

[5] Wuchty, S., and Stadler, P. F. Centers of complex networks, Journal of Theoretical Biology, 223(1), pp.45-53, (2003).

DOI: 10.1016/s0022-5193(03)00071-7

[6] Lin, C. -Y., Chin, C. -H., Wu, H. -H., Chen, S. -H., Ho, C. -W., and Ko, M. -T. Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology, Nucleic acids research, 36(suppl 2), pp. W438-W443, (2008).

DOI: 10.1093/nar/gkn257

[7] Wang, H., Li, M., Wang, J., and Pan, Y. A New Method for Identifying Essential Proteins Based on Edge Clustering Coefficient, Bioinformatics Research and Applications, J. Chen, J. Wang, and A. Zelikovsky, eds., Springer Berlin Heidelberg, pp.87-98, (2011).

DOI: 10.1007/978-3-642-21260-4_12

[8] Li, M., Wang, J., Chen, X., Wang, H., and Pan, Y. A local average connectivity-based method for identifying essential proteins from the network level, Computational biology and chemistry, 35(3), pp.143-150, (2011).

DOI: 10.1016/j.compbiolchem.2011.04.002

[9] Estrada, E., and Rodríguez-Velázquez, J. A. Subgraph centrality in complex networks, Physical Review E, 71(5), p.056103, (2005).

DOI: 10.1103/physreve.71.056103

[10] Bonacich, P. Power and Centrality: A Family of Measures, American Journal of Sociology, 92(5), pp.1170-1182, (1987).

DOI: 10.1086/228631

[11] Boser, B. E., Guyon, I. M., and Vapnik, V. N. A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, ACM, Pittsburgh, Pennsylvania, USA, pp.144-152, (1992).

DOI: 10.1145/130385.130401

[12] Cortes, C., and Vapnik, V. Support-vector networks, Mach Learn, 20(3), pp.273-297, (1995).

DOI: 10.1007/bf00994018

[13] Stark, C., Breitkreutz, B. -J., Chatr-aryamontri, A., Boucher, L., Oughtred, R., Livstone, M. S., Nixon, J., Van Auken, K., Wang, X., Shi, X., Reguly, T., Rust, J. M., Winter, A., Dolinski, K., and Tyers, M. The BioGRID Interaction Database: 2011 update, Nucleic acids research, 39(suppl 1), pp. D698-D704, (2011).

DOI: 10.1093/nar/gkq1116

[14] Mewes, H. W., Frishman, D., Mayer, K. F. X., Münsterkötter, M., Noubibou, O., Pagel, P., Rattei, T., Oesterheld, M., Ruepp, A., and Stümpflen, V. MIPS: analysis and annotation of proteins from whole genomes in 2005, Nucleic acids research, 34(suppl 1), pp. D169-D172, (2006).

DOI: 10.1093/nar/gkj148

[15] Cherry, J. M., Adler, C., Ball, C., Chervitz, S. A., Dwight, S. S., Hester, E. T., Jia, Y., Juvik, G., Roe, T., Schroeder, M., Weng, S., and Botstein, D. SGD: Saccharomyces Genome Database, Nucleic acids research, 26(1), pp.73-79, (1998).

DOI: 10.1093/nar/27.1.74

[16] Zhang, R., and Lin, Y. DEG 5. 0, a database of essential genes in both prokaryotes and eukaryotes, Nucleic acids research, 37(suppl 1), pp. D455-D458, (2009).

DOI: 10.1093/nar/gkn858

[17] Winzeler, E. A., Shoemaker, D. D., Astromoff, A., etc. Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Analysis, Science, 285(5429), pp.901-906, (1999).

[18] Chang, C. -C., and Lin, C. -J. LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol., 2(3), pp.1-27, (2011).

DOI: 10.1145/1961189.1961199