ArticleFrontiers in genetics2026
LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 methods emphasize either local neighborhoods or global variance; deep generative models cluster cell types well but often fracture trajectory continuity; and hyperbolic embeddings capture hierarchy but are numerically fragile in practice. We present LAIOR (Lorentz attentive interpretable ordinary differential equation (ODE)-regularized variational autoencoder (VAE)), a unified variational framework that combines three complementary inductive biases in a single forward pass: (i)
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