Robustness of Scaled Diffusion Models with Structural Noise Conditioning on Multimodal Tabular Data

Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data, especially with conditional control, is challenging due to its high dimensionality, sparsity, and complex biological variations. Existing generative models often struggle to capture these unique characteristics and ensure robustness to structural noise in cellular networks. We introduce LapDDPM, a novel conditional Graph Diffusion Probabilistic Model for robust and high-fidelity scRNA-seq generation. LapDDPM uniquely integrates graph-based representations with a score-based diffusion mode

Research goal: Do diffusion models with structural noise conditioning retain their robustness advantages when scaled to larger, multimodal tabular datasets, as measured by adversarial accuracy on the TabularMNLIST benchmark?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.5/10.