scLDM: a conditional diffusion framework for single-cell perturbation prediction

Clicks: 226
ID: 329979
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #86 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Motivation Accurate prediction of single-cell responses to external stimuli is pivotal for deciphering gene regulatory mechanisms and accelerating data-driven drug discovery. However, effectively capturing the complex, non-linear mapping between intrinsic cell states and external stimuli remains an open problem. Results We propose scLDM, a generative framework based on latent diffusion models for predicting single-cell perturbation responses. scLDM first compresses high-dimensional gene expression into a compact latent space via a variational autoencoder, followed by a conditional diffusion process to generate post-perturbation states, explicitly guided by pre-perturbation cellular state, cell type, and perturbation type. Systematic evaluations on six datasets across diverse biological settings, spanning pharmacological stimulation, viral infection, helminth infection, genetic perturbations, and multi-species immune response contexts, demonstrate that scLDM achieves superior predictive accuracy compared to state-of-the-art methods. Furthermore, the model exhibits strong interpretability, as the learned perturbation embeddings show high functional alignment with known biological mechanisms. Overall, scLDM provides a robust and biologically consistent strategy for in silico perturbation screening. Availability The code is available at https://github.com/samrogers1233/scLDM and archived on Zenodo at https://doi.org/10.5281/zenodo.22658164. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7214574483 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Boyang Wu, Yuhang Liu, Yue Cheng, Xiangrong Liu, Quan Zou, Leyi Wei, Lei Xu
Are you one of these authors? Claim your profile
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag727
URL
https://doi.org/10.1093/bioinformatics/btag727
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.