Abstract
Protein-protein interaction plays critical roles in cellular functions. In this paper, we propose a computational method to predict protein-protein interaction by using support vector machines and the constrained Fisher scores derived from interaction profile hidden Markov models (ipHMM) that characterize domains involved in the interaction. The constrained Fisher scores are obtained as the gradient, with respect to the model parameters, of the posterior probability for the protein to be aligned with the ipHMM as conditioned on a specified path through the model state space, in this case we used the most probable path –as determined by the Viterbi algorithm. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy measured by ROC score has shown significant improvement as compared to the previous methods.
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
Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. IEEE ASSP Magazine 3(1), 4–16 (1986)
Jaakkola, T.S., Haussler, D.: Exploiting generative models in discriminative classifiers. In: Advances in Neural Information Processing Systems, vol. 11, pp. 487–493. MIT Press, Cambridge (1998)
Jaakkola, T.S., Diekhans, M., Haussler, D.: A discriminative framework for detecting remote protein homologies. Journal of Computational Biology, 95–114 (2000)
Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis. Cambridge University Press, Cambridge (1998)
Patel, T., Liao, L.: Predicting protein-protein interaction using Fisher scores extracted from domain profiles. In: Proceedings of IEEE 7th International Symposium for Bioinformatics and Bioengineering (BIBE), Boston, MA (2007)
Kahsay, R., Gao, G., Liao, L.: Discriminating Transmembrane Proteins From Signal Peptides Using SVM-Fisher Approach. In: The Proceedings of The Fourth International Conference on Machine Learning and Applications (ICMLA 2005), Los Angeles, CA, pp. 151–155 (2005)
Finn, R., Mistry, J., Schuster-Böckler, B., Griffiths-Jones, S., Hollich, V., Lassmann, T., Moxon, S., Marshall, M., Khanna, A., Durbin, R., Eddy, S., Sonnhammer, E., Bateman, A.: Pfam: clans, web tools and services. Nucleic Acids Research (Database Issue) 34, D247–D251 (2006)
Joachims, T., Scholkopf, B., Burges, C., Smola, A.: Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1999)
Stein, A., Russell, R., Aloy, P.: 3did: interacting protein domains of known three-dimensional structure. Nucleic Acids Research 33(Database issue), D413–D417 (2005)
Itzhaki, Z., Akiva, E., Altuvia, Y., Margalit, H.: Evolutionary conservation of domain-domain interactions. Genome Biology 7, R125 (2006)
Friedrich, T., Pils, B., Dandekar, T., Schultz, J., Muller, T.: Modeling Interaction Sites in Protein Domains with Interaction Profile Hidden Markov Models. Bioinformatics 22, 2851–2857 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
González, A.J., Liao, L. (2009). Constrained Fisher Scores Derived from Interaction Profile Hidden Markov Models Improve Protein to Protein Interaction Prediction. In: Rajasekaran, S. (eds) Bioinformatics and Computational Biology. BICoB 2009. Lecture Notes in Computer Science(), vol 5462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00727-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-00727-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00726-2
Online ISBN: 978-3-642-00727-9
eBook Packages: Computer ScienceComputer Science (R0)