ArticleNature communications2021
Anti-senescent drug screening by deep learning-based morphology senescence scoring.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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.
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Who cites it
78 citing papers in PubMed, 137 citations in OpenAlex.
- Article
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- Sustainable Discovery of Natural Anti-Aging Bioactives from Food Resources: Current Status and Machine Learning Perspectives.Current issues in molecular biology · 2026Review
- Fueling the fire: metabolic dysfunction and senescence as drivers of lung aging and disease.Physiological reviews · 2026Review
- Insights into the therapeutic strategies for aging and aging-associated diseases.Signal transduction and targeted therapy · 2026Review
- Towards a context-aware framework for cellular senescence.Biogerontology · 2026Review
- Cellular Senescence in Neurodegeneration: From Cell Types to Therapeutic Opportunities.Biomedicines · 2026Review
- Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds.iScience · 2026Article
- Myocardiocyte senescence in ischemic heart disease and breathome changes.World journal of experimental medicine · 2026Review
- Monocytes are biological sensors of aging and frailty in humans.bioRxiv : the preprint server for biology · 2026Article
- AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.International journal of molecular sciences · 2026Review
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- In Vitro Fish Cell Culture: From Primary Muscle Cells to Cell-Based Meat in Cyprinidae.Biology · 2026Review
- Integrating senotherapeutics into transplantation: liver reconditioning in an aging donor pool.Transplant international : official journal of the European Society for Organ Transplantation · 2026Review
- Phenotypic Characterization Using Open-Source Deep Learning Tools.Methods in molecular biology (Clifton, N.J.) · 2026Article
- OsteoNet: A deep learning framework linking cellular morphology to molecular markers for quantifying osteogenic differentiation.Computational and structural biotechnology journal · 2026Article
- Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.Acta pharmacologica Sinica · 2025Review
- Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Prediction of the hypothalamus-pituitary organoid formation using machine learning.Cell reports methods · 2025Article
18 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
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establishment of convolutional neural networks (CNN). Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Furthermore, it is a potential therapeutic target. Specific molecular markers are used to identify senescent cells. Moreover senescent cells show unique morphology, which can be identified. We develop a successful morphology-based CNN system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells by senescence probability output from pre-trained CNN optimised for the classification of cellular senescence, Deep Learning-Based Senescence Scoring System by Morphology (Deep-SeSMo). Deep-SeSMo correctly evaluates the effects of well-known anti-senescent reagents. We screen for drugs that control cellular senescence using a kinase inhibitor library by Deep-SeSMo-based drug screening and identify four anti-senescent drugs. RNA sequence analysis reveals that these compounds commonly suppress senescent phenotypes through inhibition of the inflammatory response pathway. Thus, morphology-based CNN system can be a powerful tool for anti-senescent drug screening.
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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.