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.
Highlights
1,867 variants that resulted in amino acid changes, and/or had frameshift, stop-gained, or loss-of-function mutations were subject to further analysis
Several mutations were found in NS3, NS5A, and NS5B (Fig 2)
No significant difference was observed in the number of functional variants between specimens from sustained virological response (SVR) patients and nonSVR patients (Student’s T-test, p = 0.35, Fig 3)
Summary
We analyzed full-length HCV genomes obtained using NGS technology, developed a preliminary prediction system using several machine-learning models based on variants of the HCV genome, and evaluated the generalization performance. A total of 57 SVR and 29 nonSVR genomes (training data set) were subject to nine machine-learning algorithms and used to develop the prediction model.
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