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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, RESULTS. Meshes: Algorithms, Bayes Theorem, Cell Membrane, Computational Biology, Computer Graphics, Databases, Protein, Internet, Markov Chains, Membrane Proteins, Models, Biological, Models, Chemical, Models, Molecular, Models, Statistical, Probability, Protein Structure, Secondary, Sequence Analysis, DNA, Sequence Analysis, Protein, Software.
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Does a new decoding algorithm for hidden Markov models improve the prediction of the topology of all-beta membrane proteins? Context: Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm.
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We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins. Final Decision: yes