ArticleGenome biology2020
GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 2 of them syntheses that pooled it.
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
112 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Cross-expression meta-analysis of mouse brain slices reveals coordinated gene expression across spatially adjacent cells.Genome biology · 2025Pooled it
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- GRAIL-heart: A graph attention network for inferring ligand-receptor interactions in spatial transcriptomics.MethodsX · 2026Article
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- AutoGERN: single-cell RNA-seq gene regulatory network inference via explicit link modeling and adaptive architectures.Bioinformatics (Oxford, England) · 2026Article
- The Evolution of Spatial Omics Technologies Introduces A Novel Avenue for Lung Cancer Research.Genomics, proteomics & bioinformatics · 2026Review
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer.Nature machine intelligence · 2026Article
- Deciphering skeletal muscle development: cellular heterogeneity and molecular regulatory networks from single-cell and spatial transcriptomic perspectives.Frontiers in cell and developmental biology · 2026Review
- Graph convolutional networks for inferring cell-cell communication from spatial transcriptomics data.Bioinformatics advances · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.Nucleic acids research · 2025Article
- S3R: Modeling spatially varying associations with Spatially Smooth Sparse Regression.bioRxiv : the preprint server for biology · 2025Article
- Cell-cell interactions as predictive and prognostic markers for drug responses in cancer.Genome medicine · 2025Review
- Spatial omics enters the microscopic realm: opportunities and challenges.Trends in genetics : TIG · 2025Review
- Causality-aware graph neural networks for functional stratification and phenotype prediction at scale.NPJ systems biology and applications · 2025Article
- Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings.Nature communications · 2025Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Most methods for inferring gene-gene interactions from expression data focus on intracellular interactions. The availability of high-throughput spatial expression data opens the door to methods that can infer such interactions both within and between cells. To achieve this, we developed Graph Convolutional Neural networks for Genes (GCNG). GCNG encodes the spatial information as a graph and combines it with expression data using supervised training. GCNG improves upon prior methods used to analyze spatial transcriptomics data and can propose novel pairs of extracellular interacting genes. The output of GCNG can also be used for downstream analysis including functional gene assignment.Supporting website with software and data: https://github.com/xiaoyeye/GCNG .
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.