PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Graphical Model for Protein Secondary Structure Prediction
Wei Chu, Zoubin Ghahramani and David L Wild
In: ICML 2004, July 4-8, 2004, Whistler, Canada.

Abstract

In this paper, we present a graphical model for protein secondary structure prediction. This model extends segmental semi-Markov models (SSMM) to exploit multiple sequence alignment profiles which contain information from evolutionarily related sequences. A novel parameterized model is proposed as the likelihood function for the SSMM to capture the segmental conformation. By incorporating the information from long range interactions in beta-sheets, this model is capable of carrying out inference on contact maps. The numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements a nd the generalization performance is promising.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:783
Deposited By:Zoubin Ghahramani
Deposited On:30 December 2004