Bayesian Segmental Models with Alignment Profiles for Protein Secondary Structure Prediction
نویسندگان
چکیده
In this paper, we develop segmental semi-Markov models (SSMM) to exploit alignment profiles for protein secondary structure prediction. A novel parameterized model is proposed as the likelihood function for the SSMM to capture the segmental conformation from the profiles. By incorporating the information of long range interactions in β-sheets, this model is capable to carry 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 of this SSMM is promising.
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تاریخ انتشار 2004