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A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins
- Source :
- BMC Bioinformatics, BMC Bioinformatics, Vol 6, Iss Suppl 4, p S12 (2005)
- Publication Year :
- 2005
-
Abstract
- Background 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. 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. Conclusion 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.
- Subjects :
- Models, Molecular
MEMBRANE PROTEIN TOPOLOGY
GRAM-NEGATIVE BACTERIA
PREDICTIVE METHODS
POSTERIOR-VITERBI
Iterative Viterbi decoding
Computer science
lcsh:Computer applications to medicine. Medical informatics
Topology
Viterbi algorithm
Biochemistry
Models, Biological
Protein Structure, Secondary
symbols.namesake
Structural Biology
Sequence Analysis, Protein
Computer Graphics
Hidden Markov model
Databases, Protein
lcsh:QH301-705.5
Molecular Biology
Probability
Computer Science::Information Theory
Sequence
Internet
Models, Statistical
Applied Mathematics
Cell Membrane
Computational Biology
Membrane Proteins
Bayes Theorem
Sequence Analysis, DNA
Markov Chains
Computer Science Applications
Statistics::Computation
lcsh:Biology (General)
Models, Chemical
Path (graph theory)
symbols
lcsh:R858-859.7
Forward algorithm
Computational problem
Algorithm
Decoding methods
Algorithms
Software
Research Article
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics, BMC Bioinformatics, Vol 6, Iss Suppl 4, p S12 (2005)
- Accession number :
- edsair.doi.dedup.....c3af669acabab76e51346d2467d8d9c3