Abstract
As interest within bioinformatics has been vastly increased, efforts to predict functional role of proteins have been made using diverse approaches. In this paper, we discuss a protein function prediction method that utilizes protein molecular information including protein interaction data. The proposed method takes the given problem into account as a K-class classification problem and resolves the new problem by using a modular neural network based predictive approach. The simulation demonstrates that the proposed approach predicts the functional roles of Yeast proteins with unknown functional knowledge and is competitive to the other methodologies in KDD Cup 2001 competition.
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.
References
Clare, A., King, R.D.: Machine learning of functional class from phenotype data. Bioinformatics 18, 160–166 (2002)
Sharkey, A.J.C.: Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems. Springer, Heidelberg (1999)
Vazques, A., Flammini, A., Maritan, A., Vespignani, A.: Global protein function prediction in protein-protein interaction networks. Nature Biotechnology 21(697) (2003)
Schwikowski, B., Uetz, P., Fields, S.: A network of protein–protein interactions in yeast. Nature Biotechnology 18(3), 1257–1261 (2000)
Tucker, C.L., Gera, J.F., Uetz, P.: Towards an understanding of complex protein networks. TRENDS in cell biology 11(3), 102–106 (2001)
Uetz, P., et al.: A comprehensive analysis of protein–protein interactions in saccharomyces cerevisiae. Nature 403(10), 623–627 (2000)
Hishigaki, H., Nakai, K., Ono, T., Tanigami, T., Takagi, T.: Assessment of prediction accuracy of protein function from protein–protein interaction data. Yeast 18, 523–531 (2001)
Cheng, J., Hatzis, C., Hayashi, H., Krogel, M.A., Morishita, S., Page, D., Sese, J.: KDD Cup 2001 report. SIGKDD Exploration 3, 47–64 (2001)
Van Laer, W.: From Propositional to First Order Logic in Machine Learning and Data Mining - Induction of first order rules with ICL. PhD thesis, Department of Computer Science, Leuven, K.U., Leuven, Belgium, 239+xviii pages (June 2002)
Fellenberg, M., Albermann, K., Zollner, A., Mewes, H.W., Hani, J.: Integrative Analysis of Protein Interaction Data. In: Altmann, R., Bailey, T.L., Bourne, P., Gribskov, M., Lengauer, T., Shindyalov, I.N., Ten Eyck, L.F., Weissig, H. (eds.) Intelligent Systems for Molecular Biology, vol. 8, pp. 152–161. AAAI Press, Menlo Park (2000)
Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey, M., Haussler Jr., A.D.: Knowledge-based analysis of microarray gene expression data using support vector machines. In: Proceedings of the National Academy of Sciences (2000)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In: Proc. of the IEEE Intl. Conf. on Neural Networks, San Francisco, CA, pp. 586–591 (1993)
Japkowicz, N., Stephen, S.: The Class Imbalances: A Systematic Study. IDA Journal 6(5), 429–449 (2002)
Chawlar, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over–sampling Techniques. Journal of Artificail Intelligence Research 16, 321–357 (2002)
Oliver, S.: Guilt-by-association goes global. Nature 403(6770), 601–603 (2000)
Haykin, S.: Neural Network: A Comprehensive Foundation. Prentice Hall, Englewood Cliffs (1998)
Wang, X., Schroeder, D., Dobbs, D., Honavar, V.: Automated data-driven discovery of motif–based protein function classifiers. Information Sciences 155, 1–18 (2003)
Lu, X., Zhai, C., Gopalakrishnan, V., Buchanan, B.G.: Automatic annotation of protein motif function with Gene Ontology terms. BMC Bioinfotmatics 5(122) (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hwang, D., Kim, U., Choi, J., Park, J., Yoo, J. (2005). Predictive Model for Protein Function Using Modular Neural Approach. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_43
Download citation
DOI: https://doi.org/10.1007/11551188_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
eBook Packages: Computer ScienceComputer Science (R0)