Bayesian Protein Secondary Structure Prediction With Near-Optimal Segmentations
BibTeX
@MISC{Aydin_bayesianprotein,
author = {Zafer Aydin and Student Member and Yucel Altunbasak and Senior Member and Hakan Erdogan},
title = {Bayesian Protein Secondary Structure Prediction With Near-Optimal Segmentations},
year = {}
}
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Abstract
Abstract—Secondary structure prediction is an invaluable tool in determining the 3-D structure and function of proteins. Typically, protein secondary structure prediction methods suffer from low accuracy in-strand predictions, where nonlocal interactions play a significant role. There is a considerable need to model such longrange interactions that contribute to the stabilization of a protein molecule. In this paper, we introduce an alternative decoding technique for the hidden semi-Markov model (HSMM) originally employed in the BSPSS algorithm, and further developed in the IPSSP algorithm. The proposed method is based on the N-best paradigm where a set of most likely segmentations is computed. To generate suboptimal segmentations (i.e., alternative prediction sequences), we developed two N-best search algorithms. The first one is an stack decoder algorithm that extends paths (or hypotheses) by one symbol at each iteration. The second algorithm locally keeps
