arXiv Artificial Intelligence

LapDDPM: Spectral Perturbation Diffusion for Robust Single-Cell Manifold Generation

1 hour ago 28.09.2026 07:00 👁 1 views
LapDDPM: Spectral Perturbation Diffusion for Robust Single-Cell Manifold Generation

Quick summary

arXiv:2506.13344v2 Announce Type: replace-cross Abstract: Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data is a critical challenge in computational biology, driven by the need to model high-dimensional, sparse, and non-linear cellular manifolds. Existing generative models often fail to capture the complex topology of cellular differentiation or lack robustness against technical noise and structural variability. We introduce LapDDPM, a novel conditional Graph Diffusion Probabilistic Model designed for robust manifold learning and high-fid

Key takeaways

  • arXiv:2506.13344v2 Announce Type: replace-cross Abstract: Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data is a critical challenge in computational biology, driven by the need to model high-dimensional, sparse, and non-linear cellular manifolds.
  • Existing generative models often fail to capture the complex topology of cellular differentiation or lack robustness against technical noise and structural variability.
  • We introduce LapDDPM, a novel conditional Graph Diffusion Probabilistic Model designed for robust manifold learning and high-fid

Why it matters

“LapDDPM: Spectral Perturbation Diffusion for Robust Single-Cell Manifold Generation” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗