SIAM Journal on Computing
The consensus string problem and the complexity of comparing hidden Markov models
Journal of Computer and System Sciences - Computational biology 2002
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Two Methods for Improving Performance of a HMM and their Application for Gene Finding
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Computational Complexity of Problems on Probabilistic Grammars and Transducers
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
ExonHunter: a comprehensive approach to gene finding
Bioinformatics
Enhancements to hidden markov models for gene finding and other biological applications
Enhancements to hidden markov models for gene finding and other biological applications
The highest expected reward decoding for HMMs with application to recombination detection
CPM'10 Proceedings of the 21st annual conference on Combinatorial pattern matching
Semantics and Ambiguity of Stochastic RNA Family Models
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Hidden Markov models (HMMs) are often used for biological sequence annotation. Each sequence feature is represented by a collection of states with the same label. In annotating a new sequence, we seek the sequence of labels that has highest probability. Computing this most probable annotation was shown NP-hard by Lyngso and Pedersen [R.B. Lyngso, C.N.S. Pedersen, The consensus string problem and the complexity of comparing hidden Markov models, J. Comput. System Sci. 65 (3) (2002) 545-569]. We improve their result by showing that the problem is NP-hard for a specific HMM, and present efficient algorithms to compute the most probable annotation for a large class of HMMs, including abstractions of models previously used for transmembrane protein topology prediction and coding region detection. We also present a small experiment showing that the maximum probability annotation is more accurate than the labeling that results from simpler heuristics.