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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Bibliographic Details
Published in:PloS one Vol. 15; no. 11; p. e0242028
Main Authors: Haga, Hiroaki, Sato, Hidenori, Koseki, Ayumi, Saito, Takafumi, Okumoto, Kazuo, Hoshikawa, Kyoko, Katsumi, Tomohiro, Mizuno, Kei, Nishina, Taketo, Ueno, Yoshiyuki
Format: Journal Article
Language:English
Published: United States Public Library of Science 05.11.2020
Public Library of Science (PLoS)
Subjects:
Aged
Algorithms
Analytical methods
Antiviral agents
Antiviral Agents - therapeutic use
Antiviral drugs
Artificial Intelligence
Bayes Theorem
Bayesian analysis
Big Data
Biology and Life Sciences
Computer and Information Sciences
Datasets
Decision trees
Diagnostic systems
Discriminant analysis
Drug Therapy, Combination - methods
Female
Gastroenterology
Gene sequencing
Genetic aspects
Genetic Variation - genetics
Genome, Viral - genetics
Genomes
Genomic analysis
Health aspects
Hepacivirus - drug effects
Hepacivirus - genetics
Hepatitis
Hepatitis C
Hepatitis C - drug therapy
Hepatitis C - virology
Hepatitis C virus
Humans
Identification and classification
Learning algorithms
Machine Learning
Male
Medicine
Medicine and health sciences
Model testing
Multilayers
Neural Networks, Computer
Next-generation sequencing
Patients
Performance evaluation
Physical Sciences
Prediction models
Research and Analysis Methods
Ribonucleic acid
RNA
RNA, Viral - genetics
Statistical analysis
Statistics
Support Vector Machine
Support vector machines
Sustained Virologic Response
Testing
Training
University faculty
Viruses
Whole genome sequencing
Japan
ISSN:1932-6203, 1932-6203
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Description
Summary: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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Competing Interests: NO authors have competing interests
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0242028