Abstract
The center of attention of the research in bioinformatics has been towards understanding the biological mechanisms and protein functions. Recently high throughput experimental methods have provided many protein-protein interaction networks which need to be analyzed to provide an insight into the functional role of proteins in living organism. One of the important problems of post-genomic era is to predict the functions of unannotated proteins. In this paper we propose a novel approach for protein function prediction by utilizing the fact that most of the proteins which are connected in protein-protein interaction network, tend to have similar functions. The method randomly associates unannotated protein with functions from the possible set of functions. Our approach, Artificial Bee Colony with Temporal Difference Q-Learning (ABC-TDQL), then optimizes the score function which incorporates the extent of similarity between the set of functions of unannotated protein and annotated protein, to associate a function to an unannotated protein. The approach was utilized to predict protein function of Saccharomyces Cerevisiae and the experimental results reveal that our proposed method outperforms other algorithms in terms of precession, recall and F-value.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Shoemaker, B.A., Panchenko, A.R.: Deciphering protein-protein interactions. Part i. Experimental techniques and databases. PLoS Computational Biology 3(3), 337–344 (2007)
Breitkreutz, B.J., et al.: The BioGRID Interaction Database: 2008 Update. Nucleic Acids Research 36(Database issue), D637–D640 (2008)
Deng, M.H., Zhang, K., Mehta, S., Chen, T., Sun, F.Z.: Prediction of protein function using protein-protein interaction data. Journal of Computational Biology 10(6), 947–960 (2003)
Deng, M.H., Chen, T., Sun, F.Z.: An integrated probabilistic model for functional prediction of proteins. Journal of Computational Biology 11(2-3), 463–475 (2004)
Schwikowski, B., Uetz, P., Field, S.: A network of protein-protein interactions in yeast. Nature Biotechnology 18, 1257–1261 (2000)
Hishigaki, H., Nakai, K., Ono, T., Tanigami, A., Takagi, T.: Assessment of predition accuracy of protein function from protein-protein interaction data. Yeast 18, 523–531 (2001)
Hodgman, T.C.: A historical perspective on gene/protein functional assignment. Bioinformatics 16, 10–15 (2000)
Pearson, W.R., Lipman, D.J.: Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. U. S. A. 85, 2444–2448 (1988)
Wu, L.F., Hughes, T.R., Davierwala, A.P., Robinson, M.D., Stoughton, R., Altschuler, S.J.: Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters. Nat. Genet. 31, 255–265 (2002)
Marcotte, E.M., Pellegrini, M., Ng, H.L., Rice, D.W., Yeates, T.O., Eisenberg, D.: Detecting protein function and protein-protein interactions from genome sequences. Science 285, 751–753 (1999)
Deane, C.M., Salwinski, L., Xenarios, I., Eisenberg, D.: Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol. Cell Proteomics 1, 349–356 (2002)
Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares Jr., M., Haussler, D.: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. U. S. A. 97, 262–267 (2000)
Chen, G., Wang, J., Li, M.: GO semantic similarity based analysis for huaman protein interactions. In: Proceedings of 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, pp. 207–210 (2009)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of International Joint Conference for Artificial Intelligence, pp. 448–453 (1995)
Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxomy. In: Proceedings of International Conference Research on Computational Linguistics, pp. 19–33 (1997)
Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 296–304 (1998)
Bhowmik, P., Rakshit, P., Konar, A., Nagar, A.K., Kim, E.: DE-TDQL: an adaptive memetic algorithm. In: Congress on Evolutionary Computation, pp. 1–8 (June 2012)
Bhattacharjee, P., Rakshit, P., Goswami, I., Konar, A., Nagar, A.K.: Multi-robot path-planning using artificial bee colony optimization algorithm. In: NaBIC 2011, pp. 219–224 (2011)
Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006)
Ashburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., Cherry, J., Davis, A., Dolinski, K., Dwight, S., Eppig, J.: Gene ontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000)
Dwight, S., Harris, M., Dolinski, K., Ball, C., Binkley, G., Christie, K., Fisk, D., Issel Tarver, L., Schroeder, M., Sherlock, G.: Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO). Nucleic Acids Research 30, 69–72 (2002)
Rakshit, P., Konar, A., Das, S., Nagar, A.K.: ABC-TDQL: AN Adaptive Memetic Algorithm. In: 2013 IEEE Symposium Series on Computational Intelligence, Singapore (accepted, to be published, 2013)
Storn, R., Price, K.V.: Differential evolution–A simple and efficient adaptive scheme for global optimization over continuous spaces. Institute of Company Secretaries of India, Chennai, Tamil Nadu. Tech. Report TR-95-012 (1995)
Schwikowski, B., Uetz, P., Fields, S.: A network of protein-protein interactions in yeast. Nature Biotechnology 18, 1257–1261 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Chowdhury, A., Konar, A., Rakshit, P., Janarthanan, R. (2013). Protein Function Prediction Using Adaptive Swarm Based Algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-03756-1_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03755-4
Online ISBN: 978-3-319-03756-1
eBook Packages: Computer ScienceComputer Science (R0)