ArticleInternational journal of molecular sciences2025
Discovering Candidate Anti-Aging Perturbations Using a Foundation Model for Gene Expression.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Targeting the FOXO4-p53 axis by retro-inverso peptide senolytic agents: a pharmacological strategy to mitigate brain aging and cognitive decline.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
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Authors and funding
4 authors.
Funding
Abstract
Aging is a progressive functional decline driven by complex genetic, epigenetic, environmental, and stochastic interactions that traditional linear models struggle to capture. Using human single-cell RNA-seq data from the multi-tissue AgeAnno dataset, we fine-tuned scGPT, a large transcriptomic model, to predict chronological age groups, achieving high classification accuracy. To identify genes influencing age predictions, we systematically perturbed individual genes in silico and quantified their effects, classifying them as pro- or anti-aging candidates. Our results demonstrate that scGPT does capture age-related dependencies in single-cell data and can be utilized to discover novel candidate gene perturbations-potential targets to be validated as anti-aging interventions.
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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.