scholarly journals An Improved Hybrid Algorithm for Optimizing the Parameters of Hidden Markov Models

2021 ◽  
pp. 63-73
Author(s):  
Abukari Abdul Aziz Danaa ◽  
Mohammed Ibrahim Daabo ◽  
Alhassan Abdul-Barik
Keyword(s):  
Hidden Markov Models ◽  
Hybrid Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Real Life ◽  
Optimal Solution ◽  
Mathematical Structure ◽  
Search Space ◽  
Wide Range ◽  
Local Optimal Solution

Hidden Markov Models (HMMs) have become increasingly popular in the last several years due to the fact that, the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Various algorithms have been proposed in literature for optimizing the parameters of these models to make them applicable in real-life. However, the performance of these algorithms has remained computationally challenging largely due to slow/premature convergence and their sensitivity to preliminary estimates. In this paper, a hybrid algorithm comprising the Particle Swarm Optimization (PSO), Baum-Welch (BW), and Genetic Algorithms (GA) is proposed and implemented for optimizing the parameters of HMMs. The algorithm not only overcomes the shortcomings of the slow convergence speed of the PSO but also helps the BW escape from local optimal solution whilst improving the performance of GA despite the increase in the search space. Detailed experimental results demonstrates the effectiveness of our proposed approach when compared to other techniques available in literature.

scholarly journals Identification and Characterization of a Novel Plasmid-Encoded Laccase-Like Multicopper Oxidase from Ochrobactrum sp. BF15 Isolated from an On-Farm Bio-Purification System

2021 ◽  
Vol 59 (4) ◽  
Author(s):  
María Carla Martini ◽  
Francesca Berini ◽  
Luka Ausec ◽  
Carmine Casciello ◽  
Carolina Vacca ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
In Silico ◽  
Markov Models ◽  
Hidden Markov ◽  
Multicopper Oxidase ◽  
Industrial Applications ◽  
Activity Measurement ◽  
New Genes ◽  
Wide Range ◽  
Biotechnological Processes

Research background. In recent decades, laccases (p-diphenol-dioxygen oxidoreductases; EC 1.10.3.2) have attracted the attention of researchers due to their wide range of biotechnological and industrial applications. Laccases can oxidize a variety of organic and inorganic compounds, making them suitable as biocatalysts in biotechnological processes. Even though the most traditionally used laccases in the industry are of fungal origin, bacterial laccases have shown an enormous potential given their ability to act on several substrates and in multiple conditions. The present study aims to characterize a plasmid-encoded laccase-like multicopper oxidase (LMCO) from Ochrobactrum sp. BF15, a bacterial strain previously isolated from polluted soil. Experimental approach. We used in silico profiles Hidden Markov Models to identify novel laccase-like genes in Ochrobactrum sp. BF15. For laccase characterization, we performed heterologous expression in E. coli, purification and activity measurement on typical laccase substrates. Results and conclusions. Profiles Hidden Markov Models allowed us to identify a novel LMCO, named Lac80. In silico analysis of Lac80 revealed the presence of the three conserved copper-oxidase domains characteristic of three-domain laccases. We successfully expressed Lac80 heterologously in Escherichia coli, allowing us to purify the protein for further activity evaluation. Of thirteen typical laccase substrates tested, Lac80 showed discrete activity on 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), pyrocatechol, pyrogallol, and vanillic acid, and higher activity on 2,6-dimethoxyphenol. Novelty and scientific contribution. Our results point out Lac80 as a promising laccase for use in industrial applications. The present work shows the relevance of bacterial laccases and highlights the importance of environmental plasmids as valuable sources of new genes encoding enzymes with potential use in biotechnological processes.

scholarly journals Hidden Markov Models as a Tool for the Assessment of Dependence of Phenomena of Economic Nature

2018 ◽  
Vol 5 (338) ◽  
pp. 7-20 ◽  
Author(s):  
Michał Bernardelli
Keyword(s):  
Time Series ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Real Life ◽  
Alternative Measure ◽  
Pearson’S Correlation ◽  
Complementary Method ◽  
Pearson's Correlation ◽  
Economic Nature

The assessment of dependence between time series is a common dilemma, which is often solved by the use of the Pearson’s correlation coefficient. Unfortunately, sometimes, the results may be highly misleading. In this paper, an alternative measure is presented. It is based on hidden Markov models and Viterbi paths. The proposed method is in no way universal but seems to provide quite an accurate image of the similarities between time series, by disclosing the periods of convergence and divergence. The usefulness of this new measure is verified by specially crafted examples and real‑life macroeconomic data. There are some definite advantages to this method: the weak assumptions of applicability, ease of interpretation of the results, possibility of easy generalization, and high effectiveness in assessing the dependence of different time series of an economic nature. It should not be treated as a substitute for the Pearson’s correlation, but rather as a complementary method of dependence measure.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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