ReviewAging and disease2025
Towards Precision Aging Biology: Single-Cell Multi-Omics and Advanced AI-Driven Strategies.
Review in Aging and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- FABP4 characterizes metabolic-immune interactions and serves as a prognostic biomarker across gynecologic malignancies: a multi-omics integrative analysis.Translational oncology · 2026Article
- Hematopoietic stem cell aging: a review of transcriptional and multi-omics insights and potential paths for AI integration.Experimental & molecular medicine · 2026Review
- Inhibition of Aurora B induces senescence and potentiates immunotherapy in hepatocellular carcinoma.Cellular oncology (Dordrecht, Netherlands) · 2026Article
- Biological Age Should Anchor Age-Related Disease Research.Aging and disease · 2026Article
- Stem cell dysfunction and rejuvenation strategies in ageing: emerging advances in regenerative medicine.Frontiers in cell and developmental biology · 2026Review
- Biomaterials targeting senescent cells for bone regeneration: State-of-the-art and future perspectives.Bioactive materials · 2025Review
- NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides.BMC biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Individual aging is a complex biological process involving multiple levels, with molecular changes existing in heterogeneity across different cell types and tissues, being regulated by both internal and external factors. Traditional senescence markers, including p16, cell morphological changes, and cell cycle arrest, can only partially reflect the complexity of senescence. Single-cell omics technology facilitates the integration of multi-faceted data, including gene expression profiles, spatial dynamics, chromatin accessibility and metabolic pathways. This comprehensive approach enhances the development of biomarkers, granting us a more profound insight into the heterogeneity inherent within senescent cell populations. In this review, we summarize the application of single cell multi-omics approaches in analyzing senescence mechanisms and potential intervention targets from the perspectives of transcriptomics, epigenetics, metabolomics, and proteomics, explore the potential of developing new senescence markers at the cellular level using machine learning algorithms and artificial intelligence in bioinformatics analysis. Finally, we further discuss the challenges and prospective trajectories within this research domain to provide a more comprehensive perspective on dissecting the regulatory networks of senescence cells.
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.