ArticleHeliyon2019
A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization.
Article in Heliyon, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model.BMC bioinformatics · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.