A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus

• 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
• DOI: 10.1371/journal.pone.0242028

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.