ArticleNature communications2023
Single-cell biological network inference using a heterogeneous graph transformer.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
102 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics.Nature cell biology · 2026Article
- Personalized single-cell transcriptomics reveals molecular diversity in Alzheimer's disease.Nature communications · 2026Article
- Specificity-driven graph learning for the identification of rare cell states.Advanced biotechnology · 2026Article
- Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer.Nature communications · 2026Article
- Specificity-driven cell-gene graph learning identifies rare cell states in single-cell and spatial transcriptomic data.Advanced biotechnology · 2026Article
- Decoding apoptosis, ferroptosis, and inflammatory cell death in adenomyosis at single-cell resolution.Briefings in bioinformatics · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Integrating single-cell RNA sequencing with multi-omics to decode disease microenvironments.Molecular biology reports · 2026Review
- Statistical Methods for Detecting Nonlinear Relationships in Gene Expression and Omics Data: A Review.International journal of molecular sciences · 2026Review
- Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking.Nature communications · 2026Review
- Contrastive unlearning via representation editing for graph neural networks.Scientific reports · 2026Article
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Transcriptome graph transformer: a graph transformer-based unsupervised model for transcriptome data analysis.BMC bioinformatics · 2026Article
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
- Biased multi-view contrastive learning with attentive masking for spatial transcriptomic analysis.Briefings in bioinformatics · 2026Article
- Transformers for single-cell RNA sequencing: a survey.Briefings in bioinformatics · 2026Review
- Toward trustworthy artificial intelligence in multi-omics: a review of reproducibility, stability, and interpretability.Briefings in bioinformatics · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
42 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
20 authors.
Funding
Abstract
Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.
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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.