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Deep biomarkers of human aging: Application of deep neural networks to biomarker development
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Random Forests: Finding Quasars
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Developing a biological age assessment equation using principal component analysis and clinical biomarkers of aging in Korean men
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Identification of blood biomarkers of aging by transcript profiling of whole blood
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MARK-AGE biomarkers of ageing
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Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates
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Applications of Deep Learning in Biomedicine
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The transcriptional landscape of age in human peripheral blood
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Lower serum albumin concentration and change in muscle mass: the Health, Aging and Body Composition Study
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Glycans Are a Novel Biomarker of Chronological and Biological Ages
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The Relationship of White Blood Cell Count to Other Cardiovascular Risk Factors
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Metabolomic markers reveal novel pathways of ageing and early development in human populations
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Aging Chart: a community resource for rapid exploratory pathway analysis of age-related processes
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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DNA methylation age of human tissues and cell types
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Self-reported symptoms and healthcare seeking in the general population -exploring “The Symptom Iceberg”
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Methods for Structuring Scientific Knowledge from Many Areas Related to Aging Research
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White Blood Cell Counts as Risk Markers of Developing Metabolic Syndrome and Its Components in the Predimed Study
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Evaluation of the Automated Haematology Analyser Sysmex NE-8000
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Signaling pathway activation drift during aging: Hutchinson-Gilford Progeria Syndrome fibroblasts are comparable to normal middle-age and old-age cells
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White Blood Cells Count and Incidence of Type 2 Diabetes in Young Men
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Classifying aging as a disease in the context of ICD-11
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Association between biological aging and diabetic retinopathy
inferred← citeshttps://doi.org/10.1038/s41598-024-70696-wopenalex-snapshot
Predicting odor from vibrational spectra: a data-driven approach
inferred← citeshttps://doi.org/10.1038/s41598-025-27478-9openalex-snapshot
A sex-adjusted 7-biomarker clinical aging clock for translational preventative medicine
inferred← citeshttps://doi.org/10.1038/s41598-025-32776-3openalex-snapshot
Machine learning assessment of retinal blood flow links metabolic dysfunction and accelerated microvascular aging
inferred← citeshttps://doi.org/10.1038/s41598-025-98948-3openalex-snapshot
Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction
inferred← citeshttps://doi.org/10.1038/s41598-026-41465-8openalex-snapshot
Retinal BioAge is associated with indicators of cardiovascular-kidney-metabolic syndrome in UK and US populations
inferred← citeshttps://doi.org/10.1038/s43587-021-00082-yopenalex-snapshot
An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging
inferred← citeshttps://doi.org/10.1038/s43587-023-00415-zopenalex-snapshot
Discovering small-molecule senolytics with deep neural networks
inferred← citeshttps://doi.org/10.1042/etls20160025openalex-snapshot
Computational biology: deep learning
inferred← citeshttps://doi.org/10.1057/s10713-024-00098-5openalex-snapshot
Big data, risk classification, and privacy in insurance markets
inferred← citeshttps://doi.org/10.1073/pnas.2308812120openalex-snapshot
Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system
inferred← citeshttps://doi.org/10.1080/09540121.2022.2091103openalex-snapshot
Factors associated with early biological aging in older people with HIV
inferred← citeshttps://doi.org/10.1080/10408398.2021.1975638openalex-snapshot
Biomarkers of geroprotection and cardiovascular health: An overview of omics studies and established clinical biomarkers in the context of diet
inferred← citeshttps://doi.org/10.1080/13543784.2026.2698526openalex-snapshot
Advancements in longevity pharmacology research – are we finally seeing clinical progression?
inferred← citeshttps://doi.org/10.1080/14728222.2023.2288270openalex-snapshot
Leveraging AI to identify dual-purpose aging and disease targets
inferred← citeshttps://doi.org/10.1093/ajcp/aqaa170openalex-snapshot
The Value of Artificial Intelligence in Laboratory Medicine
inferred← citeshttps://doi.org/10.1093/bib/bbaa021openalex-snapshot
Deep learning for biological age estimation
inferred← citeshttps://doi.org/10.1093/ehjdh/ztab089openalex-snapshot
Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research
inferred← citeshttps://doi.org/10.1093/ehjdh/ztag047openalex-snapshot
Cross-sectional evaluation of cardiovascular biological age using point-of-care ultrasound
inferred← citeshttps://doi.org/10.1093/gerona/glaa151openalex-snapshot
Comparing Biological Age Estimates Using Domain-Specific Measures From the Canadian Longitudinal Study on Aging
inferred← citeshttps://doi.org/10.1093/gerona/glae006openalex-snapshot
Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases
inferred← citeshttps://doi.org/10.1093/gerona/glag032openalex-snapshot
Are aging clocks based on routine clinical indicators trustworthy and applicable? A systematic review and critical appraisal
inferred← citeshttps://doi.org/10.1093/gerona/glx096openalex-snapshot
Change in the Rate of Biological Aging in Response to Caloric Restriction: CALERIE Biobank Analysis
inferred← citeshttps://doi.org/10.1093/gerona/gly005openalex-snapshot
Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations
inferred← citeshttps://doi.org/10.1093/gerona/glz146openalex-snapshot
Estimating Biological Age in the Singapore Longitudinal Aging Study
inferred← citeshttps://doi.org/10.1093/geront/gnab058openalex-snapshot
Investigating Factors of Active Aging Among Chinese Older Adults: A Machine Learning Approach
inferred← citeshttps://doi.org/10.1093/nar/gkv1287openalex-snapshot
Aging Chart: a community resource for rapid exploratory pathway analysis of age-related processes
inferred← citeshttps://doi.org/10.1101/095653openalex-snapshot
Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes
inferred← citeshttps://doi.org/10.1101/191023openalex-snapshot
Association of a biomarker-based frailty index with telomere length in older US adults: Findings from NHANES 1999-2002
inferred← citeshttps://doi.org/10.1101/2021.07.05.21260032openalex-snapshot
Comparing the genetic and environmental architecture of blood count, blood biochemistry and urine biochemistry biological ages with machine learning
inferred← citeshttps://doi.org/10.1101/2022.03.14.484341openalex-snapshot
Deterioration of the human transcriptome with age due to increasing intron retention and spurious splicing
inferred← citeshttps://doi.org/10.1101/2022.10.19.512922openalex-snapshot
Predicting murine age across tissues and cell types using single cell transcriptome data
inferred← citeshttps://doi.org/10.1101/2022.11.26.517610openalex-snapshot
A deep neural network provides an ultraprecise multi-tissue transcriptomic clock for the short-lived fish Nothobranchius furzeri and identifies predicitive genes translatable to human aging
inferred← citeshttps://doi.org/10.1101/2023.08.21.554165openalex-snapshot
Histone mark age of human tissues and cells
inferred← citeshttps://doi.org/10.1101/2024.06.06.597715openalex-snapshot
ComputAgeBench: Epigenetic Aging Clocks Benchmark
inferred← citeshttps://doi.org/10.1101/2024.07.18.24310670openalex-snapshot
Retinal BioAge Reveals Indicators of Cardiovascular-Kidney-Metabolic Syndrome in US and UK Populations
inferred← citeshttps://doi.org/10.1101/2024.10.18.619118openalex-snapshot
Longitudinal Microbiome-based Interpretable Machine Learning for Identification of Time-Varying Biomarkers in Early Prediction of Disease Outcomes
inferred← citeshttps://doi.org/10.1101/2025.06.28.25330474openalex-snapshot
Transcriptomic age prediction using mixture-of-experts models reveals tissue-specific aging signatures in large-scale human RNA-sequencing data
inferred← citeshttps://doi.org/10.1101/2025.11.27.25341167openalex-snapshot
FusionAge framework for multimodal machine learning-based aging clocks uncovers cardiorespiratory fitness as a major driver of aging and inflammatory drivers of aging in response to spaceflight
inferred← citeshttps://doi.org/10.1101/531863openalex-snapshot
Testing for batch effect through age predictors
inferred← citeshttps://doi.org/10.1109/atsip49331.2020.9231888openalex-snapshot
Classification of DNA Microarrays Using Deep Learning to identify Cell Cycle Regulated Genes
inferred← citeshttps://doi.org/10.1109/bhi.2019.8834608openalex-snapshot
Centroid of Age Neighborhoods: A Generalized Approach to Estimate Biological Age
inferred← citeshttps://doi.org/10.1109/bibm.2018.8621108openalex-snapshot
Deep Learning-based Identification of Cancer or Normal Tissue using Gene Expression Data
inferred← citeshttps://doi.org/10.1109/bibm47256.2019.8983251openalex-snapshot
Estimating Biological Age from Physical Activity using Deep Learning with 3D CNN
inferred← citeshttps://doi.org/10.1109/bigcomp.2019.8679374openalex-snapshot
DeepFunNet: Deep Learning for Gene Functional Similarity Network Construction
inferred← citeshttps://doi.org/10.1109/cai64502.2025.00234openalex-snapshot
A Novel Modeling Focused Missing Data Imputation Framework
inferred← citeshttps://doi.org/10.1109/fmlds67896.2025.00097openalex-snapshot
VerseAge: A phenotypic age predictor based on a shallow random network architecture
inferred← citeshttps://doi.org/10.1109/icad69378.2026.11608383openalex-snapshot
Validation-Conditioned Dynamic Ensemble Regression with Applications in Biomedical Data
inferred← citeshttps://doi.org/10.1109/icassp39728.2021.9414112openalex-snapshot
Uncertainty-Based Biological Age Estimation of Brain MRI Scans
inferred← citeshttps://doi.org/10.1109/icetci55101.2022.9832074openalex-snapshot
Image Recognition Algorithm of Complex Cracks in Metro Tunnel based on Neural Network
inferred← citeshttps://doi.org/10.1109/iciss62896.2024.10751230openalex-snapshot
PSKV-System : A Framework for AI-Powered Healthy Ageing
inferred← citeshttps://doi.org/10.1109/iemcon.2018.8614888openalex-snapshot
Applying Deep Learning to Public Health: Using Unbalanced Demographic Data to Predict Thyroid Disorder
inferred← citeshttps://doi.org/10.1109/isbp57705.2023.10061311openalex-snapshot
AI Technology for Anti-Aging: an Overview
inferred← citeshttps://doi.org/10.1109/jbhi.2016.2636665openalex-snapshot
Deep Learning for Health Informatics
inferred← citeshttps://doi.org/10.1109/jbhi.2019.2930938openalex-snapshot
Centroid of Age Neighborhoods: A New Approach to Estimate Biological Age
inferred← citeshttps://doi.org/10.1109/segah49190.2020.9201820openalex-snapshot
Physical Activity Recommendation for Exergame Player Modeling using Machine Learning Approach
inferred← citeshttps://doi.org/10.1109/tmi.2021.3066857openalex-snapshot
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
inferred← citeshttps://doi.org/10.1111/acel.13080openalex-snapshot
Measuring biological aging in humans: A quest
inferred← citeshttps://doi.org/10.1111/acel.13256openalex-snapshot
Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging
inferred← citeshttps://doi.org/10.1111/acel.13320openalex-snapshot
BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy
inferred← citeshttps://doi.org/10.1111/acel.13538openalex-snapshot
Emerging rejuvenation strategies—Reducing the biological age
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