ReviewAging2019
Deep biomarkers of aging and longevity: from research to applications.
Review in Aging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Trial
- Advances in multi-omics and aging clock research for female reproductive health and aging.MedScience · 2026Review
- Leveraging Artificial Intelligence and Modulation of Oxidative Stressors to Enhance Healthspan and Radical Longevity.Biomolecules · 2025Review
- Reprogramming cellular senescence and aging clocks for advanced cancer immunotherapy.Molecular cancer · 2025Review
- Roadmap for alleviating the manifestations of ageing in the cardiovascular system.Nature reviews. Cardiology · 2025Review
- Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms.Science advances · 2024Article
- AI-Based Hematological Age Predictors and the Association Between Biological Age Acceleration and Type 2 Diabetes Mellitus - Chongqing Municipality, China, 2015-2021.China CDC weekly · 2024Article
- Animal Models Relevant for Geroscience: Current Trends and Future Perspectives in Biomarkers, and Measures of Biological Aging.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
- Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
- Article
- Why the United States needs a multifaceted definition of health.Health affairs scholar · 2023Article
- Biomedical generative pre-trained based transformer language model for age-related disease target discovery.Aging · 2023Article
- Article
- Discovery of senolytics using machine learning.Nature communications · 2023Article
- Discovering small-molecule senolytics with deep neural networks.Nature aging · 2023Article
- Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications.Brain sciences · 2023Article
- A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases.International journal of molecular sciences · 2023Article
- An Updated Overview on the Role of Small Molecules and Natural Compounds in the "Young Science" of Rejuvenation.Antioxidants (Basel, Switzerland) · 2023Review
- Towards AI-driven longevity research: An overview.Frontiers in aging · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple recent advances in machine learning enabled computer systems to exceed human performance in many tasks including voice, text, and speech recognition and complex strategy games. Aging is a complex multifactorial process driven by and resulting in the many minute changes transpiring at every level of the human organism. Deep learning systems trained on the many measurable features changing in time can generalize and learn the many biological processes on the population and individual levels. The deep age predictors can help advance aging research by establishing causal relationships in non-linear systems. Deep aging clocks can be used for identification of novel therapeutic targets, evaluating the efficacy of the various interventions, data quality control, data economics, prediction of health trajectories, mortality, and many other applications. Here we present the current state of development of the deep aging clocks in the context of the pharmaceutical research and development and clinical applications.
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.