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<p>The COVID-19 pandemic has profoundly affected global health, driven by the remarkable transmissibility and mutational adaptability of the SARS-CoV-2 virus. Although five variants of concern, Alpha, Beta, Gamma, Delta, and Omicron, have been identified, the classification task in this study is formulated using four classes: Alpha, Delta, Omicron, and Else, reflecting the sequence availability and temporal coverage of the dataset. Here, we develop an integrative framework that combines direct coupling analysis (DCA), Circos-based visualization, and convolutional neural networks (CNNs) to characterize lineage-specific epistatic signatures from large-scale SARS-CoV-2 genomic sequences. DCA-inferred pairwise mutational couplings were transformed into Circos images, which were then used as inputs for CNN-based classification models. The proposed framework achieved robust variant classification, with the best-performing model reaching a weighted-average <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(F_1\text {-score}\)</EquationSource> </InlineEquation> of <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(98.68\pm 0.75\%\)</EquationSource> </InlineEquation> and an AUC close to 1. Additional temporal holdout analyses showed that the framework retained reasonable predictive capability across evolutionary time, yielding a weighted-average <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(F_1\text {-score}\)</EquationSource> </InlineEquation> of 87.85%.</p>

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Classification of SARS-CoV-2 Variants Through the Epistatic Circos Plots with Convolutional Neural Networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Bo Jing,
  • Kai-Rui Zhang,
  • Hong-Li Zeng,
  • Erik Aurell

摘要

The COVID-19 pandemic has profoundly affected global health, driven by the remarkable transmissibility and mutational adaptability of the SARS-CoV-2 virus. Although five variants of concern, Alpha, Beta, Gamma, Delta, and Omicron, have been identified, the classification task in this study is formulated using four classes: Alpha, Delta, Omicron, and Else, reflecting the sequence availability and temporal coverage of the dataset. Here, we develop an integrative framework that combines direct coupling analysis (DCA), Circos-based visualization, and convolutional neural networks (CNNs) to characterize lineage-specific epistatic signatures from large-scale SARS-CoV-2 genomic sequences. DCA-inferred pairwise mutational couplings were transformed into Circos images, which were then used as inputs for CNN-based classification models. The proposed framework achieved robust variant classification, with the best-performing model reaching a weighted-average \(F_1\text {-score}\) of \(98.68\pm 0.75\%\) and an AUC close to 1. Additional temporal holdout analyses showed that the framework retained reasonable predictive capability across evolutionary time, yielding a weighted-average \(F_1\text {-score}\) of 87.85%.