ArticleScientific reports2020
Generating synthetic aging trajectories with a weighted network model using cross-sectional data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Demographics of co-ageing complex systems: from infected worms to chess games.Royal Society open science · 2024Article
- 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
- A complex systems approach to aging biology.Nature aging · 2022Review
- Interpretable machine learning for high-dimensional trajectories of aging health.PLoS computational biology · 2022Article
- Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: influence of features number on logistic regression model performance.European radiology · 2022Article
- The degree of frailty as a translational measure of health in aging.Nature aging · 2021Review
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Authors and funding
4 authors.
Funding
Abstract
We develop a computational model of human aging that generates individual health trajectories with a set of observed health attributes. Our model consists of a network of interacting health attributes that stochastically damage with age to form health deficits, leading to eventual mortality. We train and test the model for two different cross-sectional observational aging studies that include simple binarized clinical indicators of health. In both studies, we find that cohorts of simulated individuals generated from the model resemble the observed cross-sectional data in both health characteristics and mortality. We can generate large numbers of synthetic individual aging trajectories with our weighted network model. Predicted average health trajectories and survival probabilities agree well with the observed data.
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