A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization

Emdadi, Akram; Moughari, Fatemeh Ahmadi; Meybodi, Fatemeh Yassaee; Eslahchi, Changiz

doi:10.1016/j.heliyon.2019.e01299

Cited by 9 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For learning the parameters of the model, π, A, E, all obtained close subsequences in the first step considered as the training set for estimation of the parameters of HMM. In this study, the AntMarkov algorithm (the algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization) [ 16 ] was applied to estimate HMM parameters.…”

Section: Methodsmentioning

confidence: 99%

Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model

Emdadi

Eslahchi

2021

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

Background Predicting the response of cancer cell lines to specific drugs is an essential problem in personalized medicine. Since drug response is closely associated with genomic information in cancer cells, some large panels of several hundred human cancer cell lines are organized with genomic and pharmacogenomic data. Although several methods have been developed to predict the drug response, there are many challenges in achieving accurate predictions. This study proposes a novel feature selection-based method, named Auto-HMM-LMF, to predict cell line-drug associations accurately. Because of the vast dimensions of the feature space for predicting the drug response, Auto-HMM-LMF focuses on the feature selection issue for exploiting a subset of inputs with a significant contribution. Results This research introduces a novel method for feature selection of mutation data based on signature assignments and hidden Markov models. Also, we use the autoencoder models for feature selection of gene expression and copy number variation data. After selecting features, the logistic matrix factorization model is applied to predict drug response values. Besides, by comparing to one of the most powerful feature selection methods, the ensemble feature selection method (EFS), we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Two datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are used to indicate the efficiency of the proposed method across unseen patient cell-line. Evaluation of the proposed model showed that Auto-HMM-LMF could improve the accuracy of the results of the state-of-the-art algorithms, and it can find useful features for the logistic matrix factorization method. Conclusions We depicted an application of Auto-HMM-LMF in exploring the new candidate drugs for head and neck cancer that showed the proposed method is useful in drug repositioning and personalized medicine. The source code of Auto-HMM-LMF method is available in https://github.com/emdadi/Auto-HMM-LMF.

show abstract

Section: Methodsmentioning

confidence: 99%

Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model

Emdadi

Eslahchi

2021

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…By following the path of the study and researchers, several adjustments and implementations are produced in the years that follow. Reference [12] made an effort to estimate the HMM's parameters by assuming that the route was unknown. The Ant Colony Optimization method for estimate was modified by the authors.…”

Section: Review Of Literaturementioning

confidence: 99%

“…The Ant Colony Optimization method for estimate was modified by the authors. An effective technique for analysing biological sequences and illness prognosis has recently emerged in bioinformatics [12]. The HMM for random sequences was created and trained by [13].…”

Section: Review Of Literaturementioning

confidence: 99%

Markov Modelling for Mucoviscidosis using Genomic Data

Muthu

Kaliyaperumal

2022

EJMATH

View full text Add to dashboard Cite

The CFTR gene, which encodes a protein present in the cell membranes of epithelial tissues, has an effect on a number of organ systems in the human body. Mutations in the CFTR gene lead to incorrect regulation of cell electrolytes and water levels. The importance of this gene for typical human development has been clearly stated through studies on the CFTR mutation. A new born that inherits one mutant copy of the CFTR gene from each parent may have cystic fibrosis, which is an autosomal recessive disease. This paper establishes a model for mutant genes that will assist in determining whether or not the embedded gene is mutated. Early detection enables the possibility of slightly earlier disease risk reduction. Modelling mutant genes and correspondingly matching the new gene with them would be a crucial and cost-effective method of preventing various chronic diseases and treatment resistance.

show abstract

“…HMM depends on two main properties, which are: − The observation at time t is produced by a process whose state Ht is hidden from the observer. − The state of the hidden process represents the Markov chain [16][17][18][19][20][21][22][23].…”

Section: Hmmmentioning

confidence: 99%

Viterbi optimization for crime detection and identification

2020

View full text Add to dashboard Cite

In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way.

show abstract

A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization

Cited by 9 publications

References 33 publications

Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model

Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model

Markov Modelling for Mucoviscidosis using Genomic Data

Viterbi optimization for crime detection and identification

Contact Info

Product

Resources

About