From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks.

Area of Science:

• Computational Biology
• Genomics
• Systems Biology

Background:

• Gene regulatory networks (GRNs) are essential for understanding cellular functions and developing targeted therapies.
• Traditional GRN inference methods using bulk expression data face challenges due to high dimensionality and noise.
• Accurate GRN inference is critical for advancing biological research and therapeutic strategies.

Purpose of the Study:

• Introduce RegDiffusion, a novel computational method for inferring gene regulatory networks.
• Evaluate the performance of RegDiffusion against existing GRN inference techniques.
• Demonstrate the utility of RegDiffusion for analyzing large-scale single-cell expression data.

Main Methods:

• RegDiffusion employs a Denoising Diffusion Probabilistic Model tailored for GRN inference.
• The model introduces Gaussian noise to expression data and utilizes a neural network with a parameterized adjacency matrix to learn regulatory relationships.
• A simplified neural network architecture is used for efficient noise prediction.

Main Results:

• RegDiffusion achieves superior performance in GRN inference across multiple benchmark datasets compared to baseline methods.
• The model successfully infers biologically meaningful GRNs from complex single-cell RNA sequencing data.
• RegDiffusion demonstrates remarkable efficiency, inferring networks from datasets with over 15,000 genes in under 5 minutes.

Conclusions:

• RegDiffusion provides an effective and computationally efficient approach for gene regulatory network inference.
• Diffusion-based models show significant promise for advancing single-cell data analysis and biological discovery.
• The developed RegDiffusion tool facilitates the study of complex gene regulation in cellular systems.