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However, histopathological deep learning tools for auto\u2010identification remain poorly developed. Meanwhile, the interpretation of the computer vision attention into a cellular process is less efficient in a systematic way. Herein, it is identified that histone acetyltransferase 1 (HAT1) is an aging\u2010associated gene in the esophagus epithelium by machine learning. An interpretable deep learning model is developed to distinguish morphological changes with varied HAT1 expressions in esophageal squamous carcinoma cells (ESCC). The gradient\u2010weighted class activation mapping and prediction score analysis reveal that the computer's vision focuses on the nuclear sizes of ESCC. The hypothesized phenotype is verified in HAT1\u2010knockdown ESCCs. Finally, HAT1 regulating cell senescence by affecting the H3K27 acetylation and E2F transcription factor 7 (E2F7) expression is shown. 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