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.
Mar 14, 2007 · In this section, we discuss several scenarios from common bioinformatics applications of hidden Markov models where the multiple path problem ...
Hidden Markov models (HMMs) are often used for biological sequence annotation. Each sequence element is represented by states with the same label.
People also ask
What are the three basic problems of HMMs?
What are the applications of HMM in bioinformatics?
What is annotation in bioinformatics?
What are HMMs in bioinformatics?
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 ...
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 Lyngsø ...
Abstract. Hidden Markov models (HMMs) are often used for biological sequence annotation. Each sequence element is represented by states with the same label.
Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications.
Jun 2, 2017 · Hidden Markov models (HMMs) are used extensively in bioinformatics, and have been adapted for gene prediction, protein family classification, ...
Brown, Tomas Vinar. The most probable labeling problem in HMMs and its application to bioinformatics. Journal of Computer and System Sciences, 73(7):1060-1077.
The most probable annotation problem in HMMs and its application to bioinformatics · Tomas Vinar. Journal of Computer and System Sciences, 2007. download ...