Article

Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomics

2026-07-22

Abstract excerpt

Single-cell transcriptomics technology offers unprecedented insights into molecular heterogeneity. However, capturing sample-level representations that reflect both systemic and cellular states remains challenging, especially when disease annotations are mostly available as coarse sample-level labels. Here, we introduce Phenoverse, an interpretable deep learning framework that learns sample-level disease state rep...

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Literature Corpus work: 06b2e353-4b3a-588f-9f69-b958fcb33939
DOI: 10.64898/2026.07.21.738207

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