ArticleGenome biology2025
MaskGraphene: an advanced framework for interpretable joint representation for multi-slice, multi-condition spatial transcriptomics.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Interpretable spatial multi-omics data integration and dimensionality reduction with SpaMV.Nature communications · 2026Article
- GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.Genome biology · 2026Article
- stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images.Bioinformatics (Oxford, England) · 2026Article
- From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation.bioRxiv : the preprint server for biology · 2025Article
- MaskGraphene: an advanced framework for interpretable joint representation for multi-slice, multi-condition spatial transcriptomics.Genome biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Recent advances in spatial transcriptomics (ST) highlight the need to integrate multiple slices for joint analysis. A key challenge is generating interpretable embeddings that preserve spatial geometry while correcting batch effects. We present MaskGraphene, a graph neural network that integrates ST data via masked self-supervised learning, triplet loss, and cluster-wise local alignment. By establishing indirect "soft-links" and direct "hard-links" across slices, MaskGraphene yields joint embeddings with high geometric fidelity. Benchmarks against eight methods demonstrate superior alignment and interpretability. MaskGraphene enhances downstream applications, including domain identification, trajectory reconstruction, biomarker discovery, and brain-layer mapping, enabling robust ST integration and biological insight.
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What Socratic holds
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 Socratic graph.