Cascading Discriminant and Generative Models for Protein Secondary Structure Prediction
Most of the state-of-the-art methods for protein seconday structure prediction are complex combinations of discriminant models. They apply a local approach of the prediction which is known to induce a limit on the expected prediction accuracy. A priori, the use of generative models should make it possible to overcome this limitation. However, among the numerous hidden Markov models which have been dedicated to this task over more than two decades, none has come close to providing comparable performance. A major reason for this phenomenon is provided by the nature of the relevant information. Indeed, it is well known that irrespective of the model implemented, the prediction should benefit significantly from the availability of evolutionary information. Currently, this knowledge is embedded in position-specific scoring matrices which cannot be processed easily with hidden Markov models. With this observation at hand, the next significant advance should come from making the best of the two approaches, i.e., using a generative model on top of discriminant models. This article introduces the first hybrid architecture of this kind with state-of-the-art performance. The conjunction of the two levels of treatment makes it possible to optimize the recognition rate both at the residue level and at the segment level.
Keywordsprotein secondary structure prediction discriminant models class membership probabilities hidden Markov models
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- 3.Cole, C., Barber, J.D., Barton, G.J.: The Jpred 3 secondary structure prediction server. Nucleic Acids Research 36, W197–W201 (2008)Google Scholar
- 7.Asai, K., Hayamizu, S., Handa, K.: Prediction of protein secondary structure by the hidden Markov model. CABIOS 9, 141–146 (1993)Google Scholar
- 17.Bonidal, R., Thomarat, F., Guermeur, Y.: Estimating the class posterior probabilities in biological sequence segmentation. In: SMTDA 2012 (2012)Google Scholar
- 18.Ramesh, P., Wilpon, J.G.: Modeling state durations in hidden Markov models for automatic speech recognition. In: ICASSP 1992, pp. 381–384 (1992)Google Scholar
- 19.Guermeur, Y.: A generic model of multi-class support vector machine. International Journal of Intelligent Information and Database Systems (in press, 2012)Google Scholar
- 22.Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. Wiley, London (1989)Google Scholar
- 23.Guermeur, Y.: Combining multi-class SVMs with linear ensemble methods that estimate the class posterior probabilities. Communications in Statistics (submitted)Google Scholar
- 29.Weston, J., Watkins, C.: Multi-class support vector machines. Technical Report CSD-TR-98-04, Royal Holloway, University of London, Department of Computer Science (1998)Google Scholar
- 30.Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2, 265–292 (2001)Google Scholar