The Most Probable Labeling Problem in HMMs and Its Application to Bioinformatics

  • Broňa Brejová
  • Daniel G. Brown
  • Tomáš Vinař
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3240)


Hidden Markov models (HMMs) are often used for biological sequence annotation. Each sequence element is represented by states with the same label. A sequence should be annotated with the labeling of highest probability. Computing this most probable labeling was shown NP-hard by Lyngsø and Pedersen [9]. We improve this result by proving the problem NP-hard for a fixed HMM. High probability labelings are often found by heuristics, such as taking the labeling corresponding to the most probable state path. We introduce an efficient algorithm that computes the most probable labeling for a wide class of HMMs, including models previously used for transmembrane protein topology prediction and coding region detection.


Hide Markov Model Probable Label Emission Probability Truth Assignment Viterbi Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Broňa Brejová
    • 1
  • Daniel G. Brown
    • 1
  • Tomáš Vinař
    • 1
  1. 1.School of Computer ScienceUniversity of WaterlooWaterlooCanada

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