Journal Article

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

Hoshikawa, Kyoko, Katsumi, Tomohiro, Mizuno, Kei, Saito, Takafumi, Sato, Hidenori, Okumoto, Kazuo, Haga, Hiroaki, Nishina, Taketo, Koseki, Ayumi, Ueno, Yoshiyuki

2020

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: 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