PSSD: Progressive Spatial-Semantic Decoupling for Flow-Based Gene Expression Prediction from Histology Images

Clicks: 5
ID: 324597
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 #215 of 829 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 829 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
MOTIVATION: Predicting spatial gene expression from histology images offers a cost-effective complement to spatial transcriptomics. However, existing methods struggle to balance spatial continuity with functional heterogeneity, often producing over-smoothed predictions or neglecting spatial context. RESULTS: We present PSSD, a conditional flow matching framework with progressive spatial-semantic decoupling. PSSD models spatial and semantic information through separate but interacting pathways and employs a three-stage architecture with decoupled flows, adaptive fusion, and cross-stream coupling to generate biologically coherent, high-fidelity gene expression profiles. Across seven spatial transcriptomics datasets spanning multiple tissues and resolutions, PSSD consistently achieved the highest Pearson correlation coefficients among the compared methods while better preserving biological boundaries and spatial autocorrelation. Under the same sampling protocol, PSSD reduced inference time from 32.04 to 3.98 min per sample on DLPFC compared with the diffusion-based Stem model and achieved approximately sevenfold acceleration on the BC and cSCC datasets without compromising predictive quality. These results demonstrate that flow-based spatial-semantic decoupling provides an effective and computationally efficient bridge between histology and transcriptomics. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/ChyaZhang/PSSD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7202132223 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chengyang Zhang, Bo Li, Bob Zhang, Yuansong Zeng, Yuhao Yi, Jiancheng Lv
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag598
URL
https://doi.org/10.1093/bioinformatics/btag598
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.