LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference

Single-cell omics data are high-dimensional, sparse, and noisy, and learning embeddings that simultaneously preserve local cell-state structure, global hierarchy, and smooth developmental trajectories remains an open problem. Existing approaches typically achieve only one of these goals: classical m...

Descripció completa

Dades bibliogràfiques

Autors principals:	Zeyu Fu, Jiawei Fu, Keyang Zhang, Tianfei Ran, Chunlin Chen
Format:	Artigo
Idioma:	Inglês
Publicat:	Frontiers Media S.A. 2026-06-01
Col·lecció:	Frontiers in Genetics
Matèries:	benchmarking dynamics modeling hyperbolic geometry information bottleneck manifold learning single-cell
Accés en línia:	https://www.frontiersin.org/articles/10.3389/fgene.2026.1838613/full
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!