ArticleJournal of computational biology : a journal of computational molecular cell biology2024
From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- USP15 mutation associated with autism spectrum disorder alters progenitor fate and neuronal maturation in human brain organoids.Molecules and cells · 2026Article
- A surviving beta cell subpopulation enriched in patients with T1D.bioRxiv : the preprint server for biology · 2026Article
- Zinc accumulation-induced integrated stress response triggers β-cell identity loss.Cell research · 2026Article
- TSLP promotes GATA3-expressing effector regulatory T cells via DC2 derived from transitional DCs.Nature immunology · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Improved gene regulatory network inference from single cell data with dropout augmentation.PLoS computational biology · 2025Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Understanding gene regulatory networks (GRNs) is crucial for elucidating cellular mechanisms and advancing therapeutic interventions. Original methods for GRN inference from bulk expression data often struggled with the high dimensionality and inherent noise in the data. Here we introduce RegDiffusion, a new class of Denoising Diffusion Probabilistic Models focusing on the regulatory effects among feature variables. RegDiffusion introduces Gaussian noise to the input data following a diffusion schedule and uses a neural network with a parameterized adjacency matrix to predict the added noise. We show that using this process, GRNs can be learned effectively with a surprisingly simple model architecture. In our benchmark experiments, RegDiffusion shows superior performance compared to several baseline methods in multiple datasets. We also demonstrate that RegDiffusion can infer biologically meaningful regulatory networks from real-world single-cell data sets with over 15,000 genes in under 5 minutes. This work not only introduces a fresh perspective on GRN inference but also highlights the promising capacity of diffusion-based models in the area of single-cell analysis. The RegDiffusion software package and experiment data are available at https://github.com/TuftsBCB/RegDiffusion.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.