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A review of deep learning applications in human genomics using next-generation sequencing data
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning metho...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317091/ https://www.ncbi.nlm.nih.gov/pubmed/35879805 http://dx.doi.org/10.1186/s40246-022-00396-x |
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author | Alharbi, Wardah S. Rashid, Mamoon |
author_facet | Alharbi, Wardah S. Rashid, Mamoon |
author_sort | Alharbi, Wardah S. |
collection | PubMed |
description | Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data. |
format | Online Article Text |
id | pubmed-9317091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93170912022-07-27 A review of deep learning applications in human genomics using next-generation sequencing data Alharbi, Wardah S. Rashid, Mamoon Hum Genomics Review Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data. BioMed Central 2022-07-25 /pmc/articles/PMC9317091/ /pubmed/35879805 http://dx.doi.org/10.1186/s40246-022-00396-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Alharbi, Wardah S. Rashid, Mamoon A review of deep learning applications in human genomics using next-generation sequencing data |
title | A review of deep learning applications in human genomics using next-generation sequencing data |
title_full | A review of deep learning applications in human genomics using next-generation sequencing data |
title_fullStr | A review of deep learning applications in human genomics using next-generation sequencing data |
title_full_unstemmed | A review of deep learning applications in human genomics using next-generation sequencing data |
title_short | A review of deep learning applications in human genomics using next-generation sequencing data |
title_sort | review of deep learning applications in human genomics using next-generation sequencing data |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317091/ https://www.ncbi.nlm.nih.gov/pubmed/35879805 http://dx.doi.org/10.1186/s40246-022-00396-x |
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