ArticleNature biomedical engineering2025
A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Sex-dependent mechanisms in rheumatic diseases.Nature reviews. Rheumatology · 2026Review
- Integrative transcriptomic and experimental analysis identifies GREM1 as a pro-metastatic mediator in lung adenocarcinoma.Journal of translational medicine · 2026Article
- Reproducible-by-design: Romics Processor, a FAIR ecosystem for multi-omics and spatial-omics analysis.bioRxiv : the preprint server for biology · 2026Article
- Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent.Molecular systems biology · 2026Article
- Review
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- Hallmarks of epithelial-mesenchymal plasticity in cancer.Molecular cancer · 2026Review
- Mapping cellular and ECM heterogeneity in pulmonary fibrosis - insights from recent spatiomics studies.Cell biomaterials · 2026Article
- GRIP-Lung: Generative Model of Response to Drug-Induced Perturbation in Lung Cancer.International journal of molecular sciences · 2026Article
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- Modified RNA Extraction Methods to Eliminate Agarose Impurities in Precision-Cut Lung Slices.bioRxiv : the preprint server for biology · 2026Article
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
- Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.Nature biomedical engineering · 2025Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.BMC bioinformatics · 2025Article
- DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads.Nature communications · 2025Article
- Alveolar epithelial cell plasticity and injury memory in human pulmonary fibrosis.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
- Update of
Authors and funding
22 authors.
Funding
Abstract
Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals the accuracy of UNAGI's cellular dynamics analysis, and the use of the fibrotic cocktail-treated human precision-cut lung slices confirms UNAGI's predictions that nifedipine, an antihypertensive drug, may have anti-fibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including COVID, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond idiopathic pulmonary fibrosis, amplifying its use in the quest for therapeutic solutions across diverse pathological landscapes.
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.