ArticleBioinformatics (Oxford, England)2026
Structural-information guided fusion for spatial domain identification from spatial transcriptomics.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
motivationAccurate spatial domain identification is essential for understanding tissue organization and pathological mechanisms in spatial transcriptomics. However, existing methods mainly rely on expression profiles and spatial coordinates. Intercellular interactions are often overlooked. At the same time, preserving both local neighborhood continuity and global topological structure remains difficult.
resultsWe propose SGFST (Structural-information Guided Fusion for spatial domain identification from Spatial Transcriptomics), a novel framework for spatial domain identification in spatial transcriptomics. SGFST integrates a spatial graph and a signal graph, and employs a dual-branch graph convolutional network with attention-based fusion to capture complementary spatial and functional information. In addition, SGFST jointly optimizes a Bayesian personalized ranking loss, a zero-inflated negative binomial loss, and a distance structural information constraint to preserve local neighborhood continuity, reconstruct expression signals, and maintain global topological consistency. Experimental results on multiple datasets demonstrate that SGFST outperforms several state-of-the-art methods in spatial domain identification. AVAILABILITY AND IMPLEMENTATION: The code of SGFST is available at Github (https://github.com/xkmaxidian/SGFST) and Zenodo (DOI: 10.5281/zenodo.20624899).
Indexed as
Identifiers
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.