Improved HCV genotyping based on sequence encoding and feature integration
Year: 2026
Type: article
Abstract: Genomic sequence classification is an important task for understanding viral genetic variability and supporting genotype identification, particularly for viruses such as the Hepatitis C Virus (HCV). In this study, we evaluate the effectiveness of different sequence encoding techniques, supplementary genomic descriptors, and class-balancing strategies for HCV genotype classification under a stricter CD-HIT-EST cluster-aware evaluation protocol designed to reduce sequence-identity leakage. Four encoding methods were investigated, namely Frequency Chaos Game Representation (FCGR), k-mer encoding, one-hot encoding, and label encoding. These representations were evaluated using multiple machine learning models, including XGBoost, Random Forest, Multi-Layer Perceptron (MLP), and K-Nearest Neighbors (KNN). To address class imbalance, SMOTE was applied, while GC content and GC skew were included as supplementary compositional descriptors. Overall, FCGR and k-mer encoding outperformed one-hot and label encoding. The best result was achieved using k-mer encoding with XGBoost and SMOTE, obtaining a macro F1-score of 0.890, accuracy of 0.922, macro precision of 0.866, macro recall of 0.925, macro specificity of 0.990, and MCC of 0.893 under the CD-HIT-EST cluster-aware evaluation setting. The statistical analysis showed that SMOTE had a clear positive effect on classification performance, particularly for the Random Forest model, while GC-based descriptors provided additional interpretability but did not show a statistically significant improvement in the k-mer-based experiment. Overall, this study demonstrates the importance of selecting appropriate sequence encodings and class-balancing strategies for machine-learning-based HCV genotype classification. The findings provide a comparative basis for future work on sequence-based classification tasks in virology and computational genomics.
Source: Scientific Reports
Institutions: Nile University, Helwan University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7222808289