Interpretable deep generative ensemble learning for single-cell omics with Hydra
Abstract Single-cell omics enable the dissection of cellular heterogeneity, yet the high dimensionality, inherent noise, and sparsity present significant challenges. These challenges are amplified for rare cell populations, which are often difficult to annotate reliably but can be central to develop...
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| Principais autores: | , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Springer Nature
2026-04-01
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| coleção: | Molecular Systems Biology |
| Acesso em linha: | https://doi.org/10.1038/s44320-026-00208-7 |
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