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Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants
BACKGROUND: Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a mach...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237285/ https://www.ncbi.nlm.nih.gov/pubmed/34161886 http://dx.doi.org/10.1016/j.ebiom.2021.103322 |
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author | Luo, Xiaomei Li, Fengmei Xu, Wenchang Hong, Kaicheng Yang, Tao Chen, Jiansheng Chen, Xiaohe Wu, Hao |
author_facet | Luo, Xiaomei Li, Fengmei Xu, Wenchang Hong, Kaicheng Yang, Tao Chen, Jiansheng Chen, Xiaohe Wu, Hao |
author_sort | Luo, Xiaomei |
collection | PubMed |
description | BACKGROUND: Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1. METHODS: This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance. FINDINGS: Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted. INTERPRETATION: The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available. |
format | Online Article Text |
id | pubmed-8237285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-82372852021-06-29 Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants Luo, Xiaomei Li, Fengmei Xu, Wenchang Hong, Kaicheng Yang, Tao Chen, Jiansheng Chen, Xiaohe Wu, Hao EBioMedicine Research Paper BACKGROUND: Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1. METHODS: This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance. FINDINGS: Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted. INTERPRETATION: The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available. Elsevier 2021-06-20 /pmc/articles/PMC8237285/ /pubmed/34161886 http://dx.doi.org/10.1016/j.ebiom.2021.103322 Text en © 2021 Published by Elsevier B.V. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Paper Luo, Xiaomei Li, Fengmei Xu, Wenchang Hong, Kaicheng Yang, Tao Chen, Jiansheng Chen, Xiaohe Wu, Hao Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants |
title | Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants |
title_full | Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants |
title_fullStr | Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants |
title_full_unstemmed | Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants |
title_short | Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants |
title_sort | machine learning-based genetic diagnosis models for hereditary hearing loss by the gjb2, slc26a4 and mt-rnr1 variants |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237285/ https://www.ncbi.nlm.nih.gov/pubmed/34161886 http://dx.doi.org/10.1016/j.ebiom.2021.103322 |
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