KnvResGAT: SARS-CoV-2 sequence classification using k-mer natural vector and graph attention networks
On a time-aware per-lineage split of 182,851 curated SARS-CoV-2 genomes spanning 103 Pango lineages, KnvResGAT achieved 0.9729 accuracy and 0.9636 Macro-F1. Under the same split, it outperformed Pangolin (0.9673 accuracy, 0.9471 Macro-F1) and a strong deep baseline ResMLP (0.9654 accuracy, 0.9520 Macro-F1), demonstrating improved generalization for multi-class lineage classification.
Authors
- Wenbo Dong (ORCID: https://orcid.org/0000-0001-9451-8502)
- Yongjie Deng
- Zhewen Li
- Wenping Yu
Institutions
- Tianjin University of Science and Technology (CN)
Publication Details
- Journal
- BMC Research Notes
- Published
- 2026-02-12
- DOI
- https://doi.org/10.1186/s13104-026-07695-9
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00