ArticleiScience2026
scMILD: Single-cell multiple instance learning for sample classification and associated subpopulation discovery.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Cell-level random splits leak group-owned answers in single-cell benchmarks.bioRxiv : the preprint server for biology · 2026Article
- Annotation-free phenotype prediction using knowledge-augmented clustering from single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
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Authors and funding
3 authors.
Funding
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Abstract
Linking cellular states to clinical phenotypes is a major challenge in single-cell analysis. Here, we present single-cell multiple instance learning for sample classification and associated subpopulation discovery (scMILD), a weakly supervised multiple instance learning framework that robustly identifies condition-associated cells using only sample-level labels. After systematically validating scMILD's accuracy through controlled simulations, we applied it to diverse disease datasets, confirming its ability to retrieve known biological signatures. Building on this, our sample-informed analysis of scMILD-identified monocytes in COVID-19 revealed a temporal transition from an early antiviral to a late stress-response state. Furthermore, in a cross-disease application, a model trained on COVID-19 successfully stratified patients with Lupus and distinguished shared inflammatory states from disease-specific ones. scMILD thus provides a validated and versatile strategy to dissect cellular heterogeneity, bridging single-cell observations with high-level phenotypes.
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Registered trials
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