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Paper   IPM / Biological Sciences / 15567
School of Biological Sciences
  Title:   A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization
1.  Akram Emdadi
2.  Fatemeh Ahmadi Moughari
3.  Fatemeh Yassaee Meybodi
4.  Changiz Eslahchi
  Status:   Published
  Journal: Heliyon
  No.:  3
  Vol.:  5
  Year:  2019
  Pages:   https://doi.org/10.1016/j.heliyon.2019.e01299
  Supported by:  IPM
HMM is a powerful method to model data in various fields. Estimation of Hidden Markov Model parameters is an NP-Hard problem. We propose a heuristic algorithm called “AntMarkov” to improve the efficiency of estimating HMM parameters. We compared our method with four algorithms. The comparison was conducted on 5 different simulated datasets with different features. For further evaluation, we analyzed the performance of algorithms on the prediction of protein secondary structures problem. The results demonstrate that our algorithm obtains better results with respect to the results of the other algorithms in terms of time efficiency and the amount of similarity of estimated parameters to the original parameters and log-likelihood.
The source code of our algorithm is available in https://github.com/emdadi/HMMPE.

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