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

## TL;DR

KnvResGAT is a new method for classifying SARS-CoV-2 lineages using k-mer vectors and graph attention networks, achieving high accuracy and outperforming existing tools.

## Contribution

Introduces KnvResGAT, a novel approach combining k-mer Natural Vectors with residual GATs for improved SARS-CoV-2 lineage classification.

## Key findings

- KnvResGAT achieved 0.9729 accuracy and 0.9636 Macro-F1 on a large dataset of SARS-CoV-2 genomes.
- Outperformed Pangolin and ResMLP in multi-class lineage classification.
- Demonstrated improved generalization across 103 Pango lineages.

## Abstract

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.

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.