Research Article | | Peer-Reviewed

Exploring Hidden Markov Models in the Context of Genetic Disorders, and Related Conditions: A Systematic Review

Received: 20 April 2024     Accepted: 6 May 2024     Published: 5 July 2024
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Abstract

The application of Hidden Markov Models (HMMs) in the study of genetic and neurological disorders has shown significant potential in advancing our understanding and treatment of these conditions. This review assesses 77 papers selected from a pool of 1,105 records to evaluate the use of HMMs in disease research. After the exclusion of duplicate and irrelevant records, the papers were analyzed for their focus on HMM applications and regional representation. A notable deficiency was identified in research across regions such as Africa, South America, and Oceania, emphasizing the need for more diverse and inclusive studies in these areas. Additionally, many studies did not adequately address the role of genetic mutations in the onset and progression of these diseases, revealing a critical research gap that warrants further investigation. Future research efforts should prioritize the examination of mutations to deepen our understanding of how these changes impact the development and progression of genetic and neurological disorders. By addressing these gaps, the scientific community can facilitate the development of more effective and personalized treatments, ultimately enhancing health outcomes on a global scale. Overall, this review highlights the importance of HMMs in this area of research and underscores the necessity of broadening the scope of future studies to include a wider variety of geographical regions and a more comprehensive investigation of genetic mutations.

Published in Applied and Computational Mathematics (Volume 13, Issue 4)
DOI 10.11648/j.acm.20241304.11
Page(s) 69-82
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Hidden Markov Model, Genetic Disorders, Neurological Disorders, Mutations, Healthcare

References
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Cite This Article
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    Baranon, M. D., Weke, P. G. O., Alladatin, J., Ale, B. M., Langat, A. K. (2024). Exploring Hidden Markov Models in the Context of Genetic Disorders, and Related Conditions: A Systematic Review. Applied and Computational Mathematics, 13(4), 69-82. https://doi.org/10.11648/j.acm.20241304.11

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    ACS Style

    Baranon, M. D.; Weke, P. G. O.; Alladatin, J.; Ale, B. M.; Langat, A. K. Exploring Hidden Markov Models in the Context of Genetic Disorders, and Related Conditions: A Systematic Review. Appl. Comput. Math. 2024, 13(4), 69-82. doi: 10.11648/j.acm.20241304.11

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    AMA Style

    Baranon MD, Weke PGO, Alladatin J, Ale BM, Langat AK. Exploring Hidden Markov Models in the Context of Genetic Disorders, and Related Conditions: A Systematic Review. Appl Comput Math. 2024;13(4):69-82. doi: 10.11648/j.acm.20241304.11

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  • @article{10.11648/j.acm.20241304.11,
      author = {Mouhamadou Djima Baranon and Patrick Guge Oloo Weke and Judicael Alladatin and Boni Maxime Ale and Amos Kipkorir Langat},
      title = {Exploring Hidden Markov Models in the Context of Genetic Disorders, and Related Conditions: A Systematic Review},
      journal = {Applied and Computational Mathematics},
      volume = {13},
      number = {4},
      pages = {69-82},
      doi = {10.11648/j.acm.20241304.11},
      url = {https://doi.org/10.11648/j.acm.20241304.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20241304.11},
      abstract = {The application of Hidden Markov Models (HMMs) in the study of genetic and neurological disorders has shown significant potential in advancing our understanding and treatment of these conditions. This review assesses 77 papers selected from a pool of 1,105 records to evaluate the use of HMMs in disease research. After the exclusion of duplicate and irrelevant records, the papers were analyzed for their focus on HMM applications and regional representation. A notable deficiency was identified in research across regions such as Africa, South America, and Oceania, emphasizing the need for more diverse and inclusive studies in these areas. Additionally, many studies did not adequately address the role of genetic mutations in the onset and progression of these diseases, revealing a critical research gap that warrants further investigation. Future research efforts should prioritize the examination of mutations to deepen our understanding of how these changes impact the development and progression of genetic and neurological disorders. By addressing these gaps, the scientific community can facilitate the development of more effective and personalized treatments, ultimately enhancing health outcomes on a global scale. Overall, this review highlights the importance of HMMs in this area of research and underscores the necessity of broadening the scope of future studies to include a wider variety of geographical regions and a more comprehensive investigation of genetic mutations.},
     year = {2024}
    }
    

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Author Information
  • Department of Mathematics and Statistics, Pan African University Institute for Basic Sciences, Technology, and Innovation (PAUSTI), Nairobi, Kenya; Ecole Nationale de Statistique, de Planification et de Demographie (ENSPD), Universite de Parakou, Parakou, Benin

  • Ecole Nationale de Statistique, de Planification et de Demographie (ENSPD), Universite de Parakou, Parakou, Benin

  • School of Mathematics, University of Nairobi, Nairobi, Kenya; Faculte des Sciences de l’Education, Universite de Montreal, Montreal, Canada

  • Department of Education, Consortium Siabanni Pour la Formation, la Recherche et le Developpement (Consortium SFR-D), Abomey-Calavi, Benin; Institute of Tropical and Infectious Diseases, University of Nairobi, Nairobi, Kenya; Strathmore University Business School, Strathmore University, Nairobi, Kenya; Department of Health Research, Holo Global Health Research Institute, Nairobi, Kenya; Department of Health Research, Health Data Acumen, Nairobi, Kenya; College of Health Sciences, MOI University, Nairobi, Kenya

  • Department of Mathematics and Statistics, Pan African University Institute for Basic Sciences, Technology, and Innovation (PAUSTI), Nairobi, Kenya

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