ArticleNature methods2025
scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein-protein interactions.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Finetuning Foundation Models for Temporal Clinical Transcriptomics Data.Bioinformatics (Oxford, England) · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.Briefings in bioinformatics · 2026Article
- Supervised deep learning with gene functional annotation for cell classification.PLoS computational biology · 2026Article
- Strategies for constructing context-specific protein-protein interaction networks.Briefings in bioinformatics · 2026Review
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Targeting protein-protein interactions in Plasmodium: from asexual replication to sexual development.Parasites & vectors · 2026Review
- CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Cancer stem cell heterogeneity‑inspired therapeutics for enhancing tumor treatment outcomes (Review).International journal of oncology · 2026Review
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis.Genome biology · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Recent advances in single-cell RNA sequencing (scRNA-seq) techniques have provided unprecedented insights into the heterogeneity of various tissues. However, gene expression data alone often fails to capture and identify changes in cellular pathways and complexes, as they are more discernible at the protein level. Moreover, analyzing scRNA-seq data presents further challenges due to inherent characteristics such as high noise levels and zero inflation. In this study, we propose an approach to address these limitations by integrating scRNA-seq datasets with a protein-protein interaction network. Our method utilizes a unique dual-view architecture based on graph neural networks, enabling joint representation of gene expression and protein-protein interaction network data. This approach models gene-to-gene relationships under specific biological contexts and refines cell-cell relations using an attention mechanism. Next, through comprehensive evaluations, we demonstrate that scNET better captures gene annotation, pathway characterization and gene-gene relationship identification, while improving cell clustering and pathway analysis across diverse cell types and biological conditions.
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.