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Adv."],"published-print":{"date-parts":[[2025,8,29]]},"abstract":"<jats:p>In single-cell datasets, patient labels indicating disease status (e.g., \u201csick\u201d or \u201cnot sick\u201d) are typically available, but individual cell labels indicating which of a patient\u2019s cells are associated with their disease state are generally unknown. To address this, we introduce mixture modeling for multiple-instance learning (MMIL), an expectation-maximization approach that trains cell-level binary classifiers using only patient-level labels. Applied to primary samples from patients with acute leukemia, MMIL accurately separates leukemia from nonleukemia baseline cells, including rare minimal residual disease (MRD) cells; generalizes across tissues and treatment time points; and identifies biologically relevant features with accuracy approaching that of a hematopathologist. 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