Hepatitis C Virus Genotyping with a Transformer Neural Network

Abstract

This study aims to explore the applicability of Transformer-based models for genetic sequence classification by evaluating their performance in predicting hepatitis C virus (HCV) genotypes and subtypes after fine-tuning. A total of 2,881 HCV whole-genome sequences obtained from the Los Alamos HCV Sequence Database were used, including genotypes 1 to 6 and all confirmed subtypes. Genotypes 7 and 8 were excluded due to an insufficient number of samples. The fine-tuning process was based on several datasets that differed in fragmentation method, data volume per file, and labeling. In genotype classification, fine-tuning strategies employing homogeneous fragmentation and balanced sample distribution resulted in higher performance, with precision ranging from 98.48% to 100%. In contrast, fine-tuning conducted using a fragmentation strategy that caused data imbalance, along with an arbitrary distribution of samples across training files, achieved a precision of 48.12%, which is considered low compared with other models. This configuration, which was also manually evaluated, resulted in a high error rate in genotype 5 prediction due to its low frequency in the datasets used. In subtype classification, the best-performing fine-tuning approach achieved 99.89% accuracy and 99.87% precision. Models that included additional genotypes showed a slight decrease in performance due to the increased complexity of the task. This study demonstrates that, when fine-tuning datasets contain properly fragmented, distributed, and labeled genetic sequences, Transformer-based neural networks can achieve high performance and are a promising approach for HCV genotype and subtype classification.