Abstract
In general, the interactions between proteins are fundamental to a broad area of biological functions. In this paper, we try to predict protein-protein interactions in parallel on a 12-node PC-cluster using domains of a protein. For this, we use a hydrophobicity among protein’s amino acid’s physicochemical feature and a support vector machine (SVM) among machine learning techniques. According to the experiments, we get approximately 60% average accuracy with 5 trials and we obtained an average speed-up of 5.11 with a 12-node cluster using a proximal SVM.
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
Hopp, T.P., Woods, K.R.: Predicting of protein antigenic determinants from amino acid sequences. Proc. Natl. Acad. Sci. USA 8, 3824–3828 (1981)
Alberts, B., Bray, D., Lewis, J., Raff, M., Roberts, K., Watson, J.D.: Molecular Biology of the Cell, 2nd edn., Garland, New York (1989)
Bock, J.R., Gough, D.A.: Predicting protein–protein interactions from primary structure. Bioinformatics 17, 455–460 (2001)
Fung, G., Mangasarian, O.L.: Incremental support vector machine classification. In: 2nd SIAM int’l. Conf. on Data Mining, pp. 247–260. SIAM, Philadelphia (2002)
Chung, Y., Kim, G., Hwang, Y., Park, H.: Predicting protein–protein interactions from one feature using svm. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, Springer, Heidelberg (2004)
Chung, Y., Cho, S., Kim, C.: Predicting Protein-Protein Interactions in Parallel. In: PARA 2004 Cong. Proc., Lingby, Denmark (June 2004)
Tveit, A., Engum, H.: Parallelization of the Incremental Proximal Support Vector Machine Classifier using a Heap-based Tree Topology, Technical Report, IDI, NTNU, Trondheim, Norway (August 2003)
Anfinsen, C.B.: Principles that govern the folding of protein chains. Science 81(1), 223–230 (1973)
Joachims, T.: Making large-scale support vector machine learning practical. In: Advances in Kernel Methods-Support Vector Learning, ch. 11, pp. 169–184. MIT Press, Cambridge (1999)
Baldi, P., Brunak, S.: Bioinformatics: the machine learning approach. In: Adaptive Computation and Machine learning, MIT Press, Cambridge (1998)
Bishop, C.: Neural networks for pattern recognition. Oxford University Press, UK (1996)
Cohen, P.: Empirical methods for artificial intelligencech. 6, vol. 10, pp. 216–219. MIT Press, Cambridge (1995)
Witten, I., Frank, E.: Data mining: Practical machine learning tools with Java implementations, ch. 5, pp. 125–127. Morgan Kaufmann, San Francisco (2000)
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
Chung, Y., Cho, SY., Shin, S.Y. (2005). Parallel Prediction of Protein-Protein Interactions Using Proximal SVM. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_45
Download citation
DOI: https://doi.org/10.1007/11548706_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
Online ISBN: 978-3-540-31824-8
eBook Packages: Computer ScienceComputer Science (R0)