AIMC Topic: Aging
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Genomic determinants of biological age estimated by deep learning applied to retinal images.
GeroScience
Jan 8, 2025
With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between c...
DeepQA: A Unified Transcriptome-Based Aging Clock Using Deep Neural Networks.
Aging cell
Jan 5, 2025
Understanding the complex biological process of aging is of great value, especially as it can help develop therapeutics to prolong healthy life. Predicting biological age from gene expression data has shown to be an effective means to quantify aging ...
Predicting host health status through an integrated machine learning framework: insights from healthy gut microbiome aging trajectory.
Scientific reports
Dec 28, 2024
The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach for predicting host health status. Previous research has underscored the potential of microbiome-based...
Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms.
Science advances
Dec 18, 2024
Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomi...
Blood Biomarker Signatures for Slow Gait Speed in Older Adults: An Explainable Machine Learning Approach.
Brain, behavior, and immunity
Dec 12, 2024
Maintaining physical function is crucial for independent living in older adults, with gait speed being a key predictor of health outcomes. Blood biomarkers may potentially monitor older adults' mobility, yet their association with slow gait speed sti...
AcidAGE: a biological age determination neural network based on urine organic acids.
Biogerontology
Dec 6, 2024
Organic acids reflect the course of all important metabolic processes and the effects of diet, nutrient deficiency, lifestyle, and microbiota composition. In present work, we focused on identifying age-related changes in organic acids in urine, and c...
Signed Curvature Graph Representation Learning of Brain Networks for Brain Age Estimation.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Graph Neural Networks (GNNs) play a pivotal role in learning representations of brain networks for estimating brain age. However, the over-squashing impedes interactions between long-range nodes, hindering the ability of message-passing mechanism-bas...
Validation requirements for AI-based intervention-evaluation in aging and longevity research and practice.
Ageing research reviews
Dec 4, 2024
The field of aging and longevity research is overwhelmed by vast amounts of data, calling for the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the evaluation of geroprotective interventions. Such evaluations should...
Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend.
Nature aging
Dec 3, 2024
Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear. Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and...
Evaluating the reproducibility of a deep learning algorithm for the prediction of retinal age.
GeroScience
Nov 26, 2024
Recently, a deep learning algorithm (DLA) has been developed to predict the chronological age from retinal images. The Retinal Age Gap (RAG), a deviation between predicted age from retinal images (Retinal Age, RA) and chronological age, correlates wi...
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