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A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment
Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identify...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10398577/ https://www.ncbi.nlm.nih.gov/pubmed/37547471 http://dx.doi.org/10.3389/fgene.2023.1199087 |
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author | Wekesa, Jael Sanyanda Kimwele, Michael |
author_facet | Wekesa, Jael Sanyanda Kimwele, Michael |
author_sort | Wekesa, Jael Sanyanda |
collection | PubMed |
description | Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment. |
format | Online Article Text |
id | pubmed-10398577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103985772023-08-04 A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment Wekesa, Jael Sanyanda Kimwele, Michael Front Genet Genetics Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment. Frontiers Media S.A. 2023-07-20 /pmc/articles/PMC10398577/ /pubmed/37547471 http://dx.doi.org/10.3389/fgene.2023.1199087 Text en Copyright © 2023 Wekesa and Kimwele. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wekesa, Jael Sanyanda Kimwele, Michael A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment |
title | A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment |
title_full | A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment |
title_fullStr | A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment |
title_full_unstemmed | A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment |
title_short | A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment |
title_sort | review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10398577/ https://www.ncbi.nlm.nih.gov/pubmed/37547471 http://dx.doi.org/10.3389/fgene.2023.1199087 |
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