CancerZigZag: Iterative Seed-Anchored Diffusion for Generative Modeling of Single-Cell State Transitions

arXiv:2609.37735 · q-bio.GN · Submitted 2026-09-29 · Read on arXiv

q-bio.GN

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/JHelge/CanzerZigZag

Terminology

• Diffusion Model — 21× in this paper · explained in 8 episodes
• Sparsity — 11× in this paper · explained in 4 episodes
• Latent Representation — 9× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Latent Representations — 6× in this paper · explained in 3 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Explainability — 3× in this paper · explained in 3 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Residualization — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• Diffusion Models Beat GANs on Image Synthesis
• Denoising Diffusion Probabilistic Models

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