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Prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease
The emergence of genome-wide association studies (GWAS) has identified genetic traits and polymorphisms that are associated with the progression of nonalcoholic fatty liver disease (NAFLD). Phospholipase domain-containing 3 and transmembrane 6 superfamily member 2 are genes commonly associated with...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289529/ https://www.ncbi.nlm.nih.gov/pubmed/37363524 http://dx.doi.org/10.1097/MS9.0000000000000743 |
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author | Huang, Helen Awuah, Wireko A. Garg, Tulika Ng, Jyi Cheng Mehta, Aashna Ramamoorthy, Krishna Kalmanovich, Jacob Hasan, Mohammad M. |
author_facet | Huang, Helen Awuah, Wireko A. Garg, Tulika Ng, Jyi Cheng Mehta, Aashna Ramamoorthy, Krishna Kalmanovich, Jacob Hasan, Mohammad M. |
author_sort | Huang, Helen |
collection | PubMed |
description | The emergence of genome-wide association studies (GWAS) has identified genetic traits and polymorphisms that are associated with the progression of nonalcoholic fatty liver disease (NAFLD). Phospholipase domain-containing 3 and transmembrane 6 superfamily member 2 are genes commonly associated with NAFLD phenotypes. However, there are fewer studies and replicability in lesser-known genes such as LYPLAL1 and glucokinase regulator (GCKR). With the advent of artificial intelligence (AI) in clinical genetics, studies have utilized AI algorithms to identify phenotypes through electronic health records and utilize convolution neural networks to improve the accuracy of variant identification, predict the deleterious effects of variants, and conduct phenotype-to-genotype mapping. Natural language processing (NLP) and machine-learning (ML) algorithms are popular tools in GWAS studies and connect electronic health record phenotypes to genetic diagnoses using a combination of international classification disease (ICD)-based approaches. However, there are still limitations to machine-learning - and NLP-based models, such as the lack of replicability in larger cohorts and underpowered sample sizes, which prevent the accurate prediction of genetic variants that may increase the risk of NAFLD and its progression to advanced-stage liver fibrosis. This may be largely due to the lack of understanding of the clinical consequence in the majority of pathogenic variants. Though the concept of evolution-based AI models and evolutionary algorithms is relatively new, combining current international classification disease -based NLP models with phylogenetic and evolutionary data can improve prediction accuracy and create valuable connections between variants and their pathogenicity in NAFLD. Such developments can improve risk stratification within clinical genetics and research while overcoming limitations in GWAS studies that prevent community-wide interpretations. |
format | Online Article Text |
id | pubmed-10289529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-102895292023-06-24 Prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease Huang, Helen Awuah, Wireko A. Garg, Tulika Ng, Jyi Cheng Mehta, Aashna Ramamoorthy, Krishna Kalmanovich, Jacob Hasan, Mohammad M. Ann Med Surg (Lond) Review Articles The emergence of genome-wide association studies (GWAS) has identified genetic traits and polymorphisms that are associated with the progression of nonalcoholic fatty liver disease (NAFLD). Phospholipase domain-containing 3 and transmembrane 6 superfamily member 2 are genes commonly associated with NAFLD phenotypes. However, there are fewer studies and replicability in lesser-known genes such as LYPLAL1 and glucokinase regulator (GCKR). With the advent of artificial intelligence (AI) in clinical genetics, studies have utilized AI algorithms to identify phenotypes through electronic health records and utilize convolution neural networks to improve the accuracy of variant identification, predict the deleterious effects of variants, and conduct phenotype-to-genotype mapping. Natural language processing (NLP) and machine-learning (ML) algorithms are popular tools in GWAS studies and connect electronic health record phenotypes to genetic diagnoses using a combination of international classification disease (ICD)-based approaches. However, there are still limitations to machine-learning - and NLP-based models, such as the lack of replicability in larger cohorts and underpowered sample sizes, which prevent the accurate prediction of genetic variants that may increase the risk of NAFLD and its progression to advanced-stage liver fibrosis. This may be largely due to the lack of understanding of the clinical consequence in the majority of pathogenic variants. Though the concept of evolution-based AI models and evolutionary algorithms is relatively new, combining current international classification disease -based NLP models with phylogenetic and evolutionary data can improve prediction accuracy and create valuable connections between variants and their pathogenicity in NAFLD. Such developments can improve risk stratification within clinical genetics and research while overcoming limitations in GWAS studies that prevent community-wide interpretations. Lippincott Williams & Wilkins 2023-05-08 /pmc/articles/PMC10289529/ /pubmed/37363524 http://dx.doi.org/10.1097/MS9.0000000000000743 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/) (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Review Articles Huang, Helen Awuah, Wireko A. Garg, Tulika Ng, Jyi Cheng Mehta, Aashna Ramamoorthy, Krishna Kalmanovich, Jacob Hasan, Mohammad M. Prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease |
title | Prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease |
title_full | Prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease |
title_fullStr | Prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease |
title_full_unstemmed | Prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease |
title_short | Prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease |
title_sort | prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289529/ https://www.ncbi.nlm.nih.gov/pubmed/37363524 http://dx.doi.org/10.1097/MS9.0000000000000743 |
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