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In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. The segmental model is a generalization of the hidden Markov model where a hidden state generates segments of various length and secondary structure type. A novel parameterized model is proposed for the likelihood function that explicitly represents multiple sequence alignment profiles to capture the segmental conformation. Numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising. By incorporating the information from long range interactions in \beta{\hbox{-}}{\rm sheets}, this model is also capable of carrying out inference on contact maps. This is an important advantage of probabilistic generative models over the traditional discriminative approach to protein secondary structure prediction. The Web server of our algorithm and supplementary materials are available at
Bayesian segmental semi-Markov models, generative models, protein secondary structure, contact maps, multiple sequence alignment profiles, parametric models.

D. L. Wild, Z. Ghahramani, A. Podtelezhnikov and W. Chu, "Bayesian Segmental Models with Multiple Sequence Alignment Profiles for Protein Secondary Structure and Contact Map Prediction," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. , pp. 98-113, 2006.
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