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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\n                    <jats:inline-formula>\n                      <jats:tex-math>$$F_1\\text {-score}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    of\n                    <jats:inline-formula>\n                      <jats:tex-math>$$98.68\\pm 0.75\\%$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    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\n                    <jats:inline-formula>\n                      <jats:tex-math>$$F_1\\text {-score}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    of 87.85%.\n                  <\/jats:p>","DOI":"10.1007\/s00239-026-10329-0","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T09:12:55Z","timestamp":1783761175000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of SARS-CoV-2 Variants Through the Epistatic Circos Plots with Convolutional Neural Networks"],"prefix":"10.1007","author":[{"given":"Bo","family":"Jing","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai-Rui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Li","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik","family":"Aurell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,11]]},"reference":[{"key":"10329_CR1","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20156651","volume":"12","author":"C Angermueller","year":"2016","unstructured":"Angermueller C, P\u00e4rnamaa T, Parts L, Stegle O (2016) Deep learning for computational biology. 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