ReviewBiogerontology2017
A review of supervised machine learning applied to ageing research.
Review in Biogerontology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses 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
49 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The multiple uses of artificial intelligence in exercise programs: a narrative review.Frontiers in public health · 2025Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging.Experimental gerontology · 2023Pooled it
- Does Lower Regression Error Mean Stronger Forensic Evidence? Machine Learning Regression Versus Demirjian and Willems Methods for Dental Age Estimation at 12- and 15-Year Legal Thresholds.Diagnostics (Basel, Switzerland) · 2026Article
- From mechanistic models to artificial intelligence: exploring the potential of digital twins in geriatric oncology.Frontiers in artificial intelligence · 2026Article
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Leveraging Artificial Intelligence and Modulation of Oxidative Stressors to Enhance Healthspan and Radical Longevity.Biomolecules · 2025Review
- Development of a microRNA-Based age estimation model using whole-blood microRNA expression profiling.Non-coding RNA research · 2025Article
- EXO1 overexpression induces homologous recombination deficiency and enhances PARP inhibitor sensitivity in ER-positive breast cancer: modulation by N4BP2L2-Mediated restoration.Frontiers in cell and developmental biology · 2025Article
- Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks.NAR genomics and bioinformatics · 2024Article
- Using non-invasive behavioral and physiological data to measure biological age in wild baboons.GeroScience · 2024Article
- Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis.PloS one · 2024Article
- Artificial intelligence in positive mental health: a narrative review.Frontiers in digital health · 2024Review
- Article
- Autophagy dark genes: Can we find them with machine learning?Natural sciences (Weinheim, Germany) · 2023Article
- Machine learning-based predictions of dietary restriction associations across ageing-related genes.BMC bioinformatics · 2022Article
- The Inflamm-Aging Model Identifies Key Risk Factors in Atherosclerosis.Frontiers in genetics · 2022Article
- Machine Learning-Based Prediction of Subsequent Vascular Events After 6 Months in Chinese Patients with Minor Ischemic Stroke.International journal of general medicine · 2022Article
- DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning.Computational intelligence and neuroscience · 2022Article
- Towards future directions in data-integrative supervised prediction of human aging-related genes.Bioinformatics advances · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.
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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.