Brona Brejova, Daniel G. 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. 2007. Early version in WABI 2004.
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Download from publisher: http://dx.doi.org/10.1016/j.jcss.2007.03.011
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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 Lyngsoe and Pedersen. 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.