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Objective <p>We propose KnvResGAT for efficient SARS-CoV-2 lineage classification by combining k-mer Natural Vector (KNV) representations with a residual multi-head Graph Attention Network (GAT) on a k-nearest-neighbor (kNN) similarity graph constructed in the KNV feature space.</p> Results <p>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.</p>

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KnvResGAT: SARS-CoV-2 sequence classification using k-mer natural vector and graph attention networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月12日

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  • Wenping Yu,
  • Yongjie Deng,
  • Zhewen Li,
  • Wenbo Dong

摘要

Objective

We propose KnvResGAT for efficient SARS-CoV-2 lineage classification by combining k-mer Natural Vector (KNV) representations with a residual multi-head Graph Attention Network (GAT) on a k-nearest-neighbor (kNN) similarity graph constructed in the KNV feature space.

Results

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.