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...
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| Vydané v: | PloS one Ročník 15; číslo 11; s. e0242028 |
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| Hlavní autori: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Public Library of Science
05.11.2020
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Hoshikawa, Kyoko Katsumi, Tomohiro Mizuno, Kei Saito, Takafumi Sato, Hidenori Okumoto, Kazuo Haga, Hiroaki Nishina, Taketo Koseki, Ayumi Ueno, Yoshiyuki |
| AuthorAffiliation | 2 Genome Informatics Unit, Institute for Promotion of Medical Science Research, Yamagata University, Yamagata, Japan Nihon University School of Medicine, JAPAN 1 Department of Gastroenterology, Yamagata University Faculty of Medicine, Yamagata, Japan 3 School of Nursing, Yamagata University Faculty of Medicine, Yamagata, Japan |
| AuthorAffiliation_xml | – name: 1 Department of Gastroenterology, Yamagata University Faculty of Medicine, Yamagata, Japan – name: Nihon University School of Medicine, JAPAN – name: 3 School of Nursing, Yamagata University Faculty of Medicine, Yamagata, Japan – name: 2 Genome Informatics Unit, Institute for Promotion of Medical Science Research, Yamagata University, Yamagata, Japan |
| Author_xml | – sequence: 1 givenname: Hiroaki orcidid: 0000-0001-9355-1837 surname: Haga fullname: Haga, Hiroaki – sequence: 2 givenname: Hidenori surname: Sato fullname: Sato, Hidenori – sequence: 3 givenname: Ayumi surname: Koseki fullname: Koseki, Ayumi – sequence: 4 givenname: Takafumi surname: Saito fullname: Saito, Takafumi – sequence: 5 givenname: Kazuo surname: Okumoto fullname: Okumoto, Kazuo – sequence: 6 givenname: Kyoko surname: Hoshikawa fullname: Hoshikawa, Kyoko – sequence: 7 givenname: Tomohiro surname: Katsumi fullname: Katsumi, Tomohiro – sequence: 8 givenname: Kei surname: Mizuno fullname: Mizuno, Kei – sequence: 9 givenname: Taketo surname: Nishina fullname: Nishina, Taketo – sequence: 10 givenname: Yoshiyuki orcidid: 0000-0001-5623-4250 surname: Ueno fullname: Ueno, Yoshiyuki |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33152046$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1186_s12859_023_05456_0 crossref_primary_10_1093_clinchem_hvab239 crossref_primary_10_1016_j_heliyon_2024_e32061 crossref_primary_10_29333_ejgm_15747 crossref_primary_10_3390_life13010079 crossref_primary_10_1016_j_addr_2021_113922 crossref_primary_10_1007_s44174_025_00341_1 crossref_primary_10_1002_ima_22746 crossref_primary_10_1007_s42835_023_01441_y crossref_primary_10_1007_s10096_025_05110_y crossref_primary_10_1007_s10238_025_01811_y crossref_primary_10_1016_j_csbj_2021_07_021 crossref_primary_10_1097_JS9_0000000000000548 crossref_primary_10_1016_j_datak_2023_102147 crossref_primary_10_3390_ijerph20032380 crossref_primary_10_1007_s10142_024_01289_z crossref_primary_10_3748_wjg_v27_i37_6191 crossref_primary_10_1053_j_gastro_2025_05_012 crossref_primary_10_1002_rmv_2514 crossref_primary_10_1016_j_tim_2025_04_017 crossref_primary_10_2196_60207 |
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| Copyright | COPYRIGHT 2020 Public Library of Science 2020 Haga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Haga et al 2020 Haga et al |
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| Title | A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus |
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