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A profile Hidden Markov Model (HMM) is well suited for representing profiles of multiple sequences alignments, and it has been becoming the main method of multiple sequences alignments in bioinformatics. The scoring of sequences on profile HMMs is compute-intensive, especially when there are many Markov models and many states in each model. A parallel algorithm for Graphic Processing Unit (GPU)s is...
A parallel algorithm on graphic processing units is presented to evaluate an observation sequence on hidden Markov models quickly. The evaluation is compute-intensive when there are thousands of or more Markov models and there are many states in each model. The compute-load can be reduced by the proposed algorithm greatly, but the hardware needed by the algorithm is not so expensive as the other accelerating...
It is compute-intensive to evaluate the probability of an observation sequence on a hidden Markov model. Some fast algorithms exit, the forward-backward procedure is the most popular one among them. The forward-backward procedure can save much computation, but its time complexity is N2T, in other words, there is a high computational complexity in the algorithm. In this paper, we present a parallel...
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