Document Type
Article
Publication Date
2026
DOI
10.1038/s41598-026-53625-x
Publication Title
Scientific Reports
Volume
Advance online publication
Pages
22 pp.
Abstract
Identifying informative genomic features in SARS-CoV-2 can help clarify patterns of viral evolution. In this study, we developed an explainable convolutional neural network (CNN) model to classify SARS-CoV-2 genomic sequences into the WHO-designated Variants of Concern (VOCs), Alpha, Beta, Gamma, Delta, and Omicron. Using a balanced dataset of genomes, the classification CNN achieved 99.96% accuracy on the held-out test set. To interpret the model’s predictions, we applied SHapley Additive exPlanations (SHAP) to estimate the contribution of each nucleotide position to VOC-label prediction and compared aggregated attributions with a chi-square GWAS baseline applied to the same categorical labels. SHAP prioritized several lineage-associated sites in Spike, including C23525T (S: H655Y) and A21801C (S: D80A), and also highlighted ORF8, ORF9, and intergenic positions that were not detected in the chi-square GWAS baseline. Based on the comparison between the CNN and GWAS, 23.8%–32.4% of top-ranked positions overlapped, with the shared subset enriched in Spike. We interpret these results as evidence that explainable deep learning can complement site-wise association analysis. This work therefore serves as a proof-of-concept that convolutional neural network modeling with post hoc attribution can provide an alternative genome-wide association perspective on mutations in SARS-CoV-2.
Rights
© 2026 The Authors.
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Original Publication Citation
Hatami, P., Annan, R., Miranda, L., Gorman, J., Xie, M., Qingge, L., & Qin, H. (2026). Explainable convolutional neural network model provides an alternative genome-wide association perspective on mutations in SARS-CoV-2. Scientific Reports. Advance online publication. https://doi.org/10.1038/s41598-026-53625-x
Repository Citation
Hatami, P., Annan, R., Miranda, L., Gorman, J., Xie, M., Qingge, L., & Qin, H. (2026). Explainable convolutional neural network model provides an alternative genome-wide association perspective on mutations in SARS-CoV-2. Scientific Reports. Advance online publication. https://doi.org/10.1038/s41598-026-53625-x
ORCID
0000-0002-1060-6722 (Qin)
Supplementary Material 1