A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus
| Autor: | Ayumi Koseki, Taketo Nishina, Tomohiro Katsumi, Kyoko Hoshikawa, Takafumi Saito, Hidenori Sato, Hiroaki Haga, Yoshiyuki Ueno, Kei Mizuno, Kazuo Okumoto |
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| Rok vydání: | 2020 |
| Předmět: |
Male
RNA viruses 0301 basic medicine Support Vector Machine Sustained Virologic Response Molecular biology Computer science Hepacivirus medicine.disease_cause computer.software_genre Genome Machine Learning Virological response Bayes' theorem Mathematical and Statistical Techniques Sequencing techniques 0302 clinical medicine DNA sequencing Pathology and laboratory medicine Multidisciplinary Artificial neural network Hepatitis C virus Applied Mathematics Simulation and Modeling Statistics virus diseases Genomics Medical microbiology Hepatitis C Physical Sciences Viruses Amino Acid Analysis RNA Viral Medicine Drug Therapy Combination Female 030211 gastroenterology & hepatology Pathogens Transcriptome Analysis Algorithms Research Article Next-Generation Sequencing Computer and Information Sciences Science Decision tree Genome Viral Research and Analysis Methods Machine learning Antiviral Agents Microbiology Machine Learning Algorithms 03 medical and health sciences Naive Bayes classifier Artificial Intelligence Support Vector Machines Genetics medicine Humans Statistical Methods Selection (genetic algorithm) Aged Medicine and health sciences Whole genome sequencing Molecular Biology Assays and Analysis Techniques Flaviviruses business.industry Organisms Viral pathogens Genetic Variation Biology and Life Sciences Computational Biology Cancer Bayes Theorem Genome Analysis Perceptron medicine.disease Hepatitis viruses digestive system diseases Microbial pathogens Support vector machine Data set Molecular biology techniques 030104 developmental biology Neural Networks Computer Artificial intelligence business computer Mathematics Forecasting |
| Zdroj: | PLoS ONE, Vol 15, Iss 11, p e0242028 (2020) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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