Biology – Quantitative Biology – Biomolecules
Scientific paper
2005-01-04
Biology
Quantitative Biology
Biomolecules
23 pages, 3 figures
Scientific paper
Background: Hidden Markov models (HMM) are powerful machine learning tools successfully applied to problems of computational Molecular Biology. 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 class labeling, 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 automaton 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. Results: 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. Conclusions: We show that PV decoding performs better than other algorithms first on toy models and then on the computational biological problem of the prediction of the topology of beta-barrel membrane proteins.
Casadio Rita
Fariselli Piero
Martelli Pier Luigi
No associations
LandOfFree
The posterior-Viterbi: a new decoding algorithm for hidden Markov models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with The posterior-Viterbi: a new decoding algorithm for hidden Markov models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and The posterior-Viterbi: a new decoding algorithm for hidden Markov models will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-529601