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.Comment: 23 pages, 3 figure
