ReviewBriefings in bioinformatics2021
Deep learning for biological age estimation.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- From aging biology to cardiac biotechnology: emerging platforms for modeling cardiac aging.JCI insight · 2026Review
- Artificial Intelligence Applications in Gastric Cancer Surgery: Bridging Early Diagnosis and Responsible Precision Medicine.Journal of clinical medicine · 2026Review
- When Stones Begin: Age of Onset Reveals the Systemic Metabolic Nature of Nephrolithiasis.Research and reports in urology · 2026Article
- Artificial intelligence approaches in biological age prediction: current status and challenges.British journal of biomedical science · 2026Review
- Biologically informed dual deep learning for skeletal maturity prediction in pediatrics.Frontiers in radiology · 2026Article
- Deep-DPC: Deep learning-assisted label-free temporal imaging discovery of anti-fibrotic compounds by controlling cell morphology.Journal of advanced research · 2025Article
- Biological Age Estimation From the Age Gap Using Deep Learning Integrating Morbidity and Mortality: Model Development and Validation Study.Journal of medical Internet research · 2025Article
- The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.International journal of legal medicine · 2025Article
- Association of accelerated biological aging with kidney stone: Insights from the U.S. National health and nutrition examination survey.Preventive medicine reports · 2025Article
- Cardiovascular Risk Assessment via Sleep Patterns and ECG-Based Biological Age Estimation.Journal of clinical medicine · 2025Article
- Risk factors for death in patients with acute diquat poisoning.World journal of emergency medicine · 2025Article
- Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan.Diagnostics (Basel, Switzerland) · 2025Article
- Biological age prediction and NAFLD risk assessment: a machine learning model based on a multicenter population in Nanchang, Jiangxi, China.BMC gastroenterology · 2025Article
- Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation.International journal of legal medicine · 2025Review
- Reclassification of the conventional risk assessment for aging-related diseases by electrocardiogram-enabled biological age.npj aging · 2025Article
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
- Biological age in critical care: current evidence, future prospects, and clinical implications.Frontiers in medicine · 2025Review
- Multivariate analysis of immunosenescence data in healthy humans and diverse diseases.Frontiers in aging · 2025Article
- AcidAGE: a biological age determination neural network based on urine organic acids.Biogerontology · 2024Article
- Interplay Between Skeletal and Hematopoietic Cells in the Bone Marrow Microenvironment in Homeostasis and Aging.Current osteoporosis reports · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health.
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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.