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3512 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 55, NO. 7, JULY 2007 Bayesian Protein Secondary Structure Prediction
 

Summary: 3512 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 55, NO. 7, JULY 2007
Bayesian Protein Secondary Structure Prediction
With Near-Optimal Segmentations
Zafer Aydin, Student Member, IEEE, Yucel Altunbasak, Senior Member, IEEE, and Hakan Erdogan, Member, IEEE
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 long-
range interactions that contribute to the stabilization of a protein
molecule. In this paper, we introduce an alternative decoding tech-
nique for the hidden semi-Markov model (HSMM) originally em-
ployed 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
the end positions of the highest scoring previous segments and

  

Source: Aydin, Zafer - Department of Genome Sciences, University of Washington at Seattle
Erdogan, Hakan - Faculty of Engineering and Natural Sciences, Sabanci University

 

Collections: Biology and Medicine; Energy Storage, Conversion and Utilization