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ICML
2004
IEEE

A graphical model for protein secondary structure prediction

10 years 3 months ago
A graphical model for protein secondary structure prediction
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 -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 and the generalization performance is promising.
Wei Chu, Zoubin Ghahramani, David L. Wild
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Wei Chu, Zoubin Ghahramani, David L. Wild
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