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A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus
In recent years, the development of diagnostics using artificial intelligence (AI) has been remarkable. AI algorithms can go beyond human reasoning and build diagnostic models from a number of complex combinations. Using next-generation sequencing technology, we identified hepatitis C virus (HCV) va...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644079/ https://www.ncbi.nlm.nih.gov/pubmed/33152046 http://dx.doi.org/10.1371/journal.pone.0242028 |
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author | Haga, Hiroaki Sato, Hidenori Koseki, Ayumi Saito, Takafumi Okumoto, Kazuo Hoshikawa, Kyoko Katsumi, Tomohiro Mizuno, Kei Nishina, Taketo Ueno, Yoshiyuki |
author_facet | Haga, Hiroaki Sato, Hidenori Koseki, Ayumi Saito, Takafumi Okumoto, Kazuo Hoshikawa, Kyoko Katsumi, Tomohiro Mizuno, Kei Nishina, Taketo Ueno, Yoshiyuki |
author_sort | Haga, Hiroaki |
collection | PubMed |
description | In recent years, the development of diagnostics using artificial intelligence (AI) has been remarkable. AI algorithms can go beyond human reasoning and build diagnostic models from a number of complex combinations. Using next-generation sequencing technology, we identified hepatitis C virus (HCV) variants resistant to directing-acting antivirals (DAA) by whole genome sequencing of full-length HCV genomes, and applied these variants to various machine-learning algorithms to evaluate a preliminary predictive model. HCV genomic RNA was extracted from serum from 173 patients (109 with subsequent sustained virological response [SVR] and 64 without) before DAA treatment. HCV genomes from the 109 SVR and 64 non-SVR patients were randomly divided into a training data set (57 SVR and 29 non-SVR) and a validation-data set (52 SVR and 35 non-SVR). The training data set was subject to nine machine-learning algorithms selected to identify the optimized combination of functional variants in relation to SVR status following DAA therapy. Subsequently, the prediction model was tested by the validation-data set. The most accurate learning method was the support vector machine (SVM) algorithm (validation accuracy, 0.95; kappa statistic, 0.90; F-value, 0.94). The second-most accurate learning algorithm was Multi-layer perceptron. Unfortunately, Decision Tree, and Naive Bayes algorithms could not be fitted with our data set due to low accuracy (< 0.8). Conclusively, with an accuracy rate of 95.4% in the generalization performance evaluation, SVM was identified as the best algorithm. Analytical methods based on genomic analysis and the construction of a predictive model by machine-learning may be applicable to the selection of the optimal treatment for other viral infections and cancer. |
format | Online Article Text |
id | pubmed-7644079 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-76440792020-11-16 A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus Haga, Hiroaki Sato, Hidenori Koseki, Ayumi Saito, Takafumi Okumoto, Kazuo Hoshikawa, Kyoko Katsumi, Tomohiro Mizuno, Kei Nishina, Taketo Ueno, Yoshiyuki PLoS One Research Article In recent years, the development of diagnostics using artificial intelligence (AI) has been remarkable. AI algorithms can go beyond human reasoning and build diagnostic models from a number of complex combinations. Using next-generation sequencing technology, we identified hepatitis C virus (HCV) variants resistant to directing-acting antivirals (DAA) by whole genome sequencing of full-length HCV genomes, and applied these variants to various machine-learning algorithms to evaluate a preliminary predictive model. HCV genomic RNA was extracted from serum from 173 patients (109 with subsequent sustained virological response [SVR] and 64 without) before DAA treatment. HCV genomes from the 109 SVR and 64 non-SVR patients were randomly divided into a training data set (57 SVR and 29 non-SVR) and a validation-data set (52 SVR and 35 non-SVR). The training data set was subject to nine machine-learning algorithms selected to identify the optimized combination of functional variants in relation to SVR status following DAA therapy. Subsequently, the prediction model was tested by the validation-data set. The most accurate learning method was the support vector machine (SVM) algorithm (validation accuracy, 0.95; kappa statistic, 0.90; F-value, 0.94). The second-most accurate learning algorithm was Multi-layer perceptron. Unfortunately, Decision Tree, and Naive Bayes algorithms could not be fitted with our data set due to low accuracy (< 0.8). Conclusively, with an accuracy rate of 95.4% in the generalization performance evaluation, SVM was identified as the best algorithm. Analytical methods based on genomic analysis and the construction of a predictive model by machine-learning may be applicable to the selection of the optimal treatment for other viral infections and cancer. Public Library of Science 2020-11-05 /pmc/articles/PMC7644079/ /pubmed/33152046 http://dx.doi.org/10.1371/journal.pone.0242028 Text en © 2020 Haga et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Haga, Hiroaki Sato, Hidenori Koseki, Ayumi Saito, Takafumi Okumoto, Kazuo Hoshikawa, Kyoko Katsumi, Tomohiro Mizuno, Kei Nishina, Taketo Ueno, Yoshiyuki A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus |
title | A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus |
title_full | A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus |
title_fullStr | A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus |
title_full_unstemmed | A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus |
title_short | A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus |
title_sort | machine learning-based treatment prediction model using whole genome variants of hepatitis c virus |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644079/ https://www.ncbi.nlm.nih.gov/pubmed/33152046 http://dx.doi.org/10.1371/journal.pone.0242028 |
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