Exploring phenotype-related single-cells through attention-enhanced representation learning

Abstract Background Atlas-level single-cell investigations elucidate disease pathogenesis and progression. Accurate interpretation of phenotype-related single-cell data necessitates pre-defining cell subtypes and identifying their abundance variations. However, batch correction and clustering resolu...

Descripción completa

Detalles Bibliográficos

Autores principales:	Qinhua Wu, Junxiang Ding, Ruikun He, Lijian Hui, Junwei Liu, Yixue Li
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	BMC 2026-01-01
Colección:	Genome Medicine
Materias:	Single-cell analysis Multiple Instance Learning (MIL) Phenotype prediction Mixture-of-Experts (MoE) Domain adaptation
Acceso en línea:	https://doi.org/10.1186/s13073-026-01598-x
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!