ArticleNature communications2022
Unsupervised learning of aging principles from longitudinal data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Embracing non-linearity in human ageing.Nature reviews. Genetics · 2026Review
- Article
- Old worms, new tricks: dynamical instability explains late-life rejuvenation inbioRxiv : the preprint server for biology · 2026Article
- Nurses as guardians of time: the hidden clinical value of continuous care in geriatrics.Frontiers in public health · 2026Review
- How and why does aging occur? Updating evolutionary theory to meet a new era of data.Evolution, medicine, and public health · 2026Article
- Systematically Revealing Quantitative Multi-Target Integrative Effects of Plants With Artificial Intelligence Method.Plant biotechnology journal · 2025Article
- A hematology-based clock derived from the Study of Longitudinal Aging in Mice to estimate biological age.Nature aging · 2024Article
- Dynamical Network Stability Analysis of Multiple Biological Ages Provides a Framework for Understanding the Aging Process.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Article
- Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention.Nature aging · 2024Article
- Network dynamical stability analysis reveals key "mallostatic" natural variables that erode homeostasis and drive age-related decline of health.Scientific reports · 2023Article
- How is Big Data reshaping preclinical aging research?Lab animal · 2023Review
- Review
- Biological age is increased by stress and restored upon recovery.Cell metabolism · 2023Article
- Unsupervised learning of aging principles from longitudinal data.Nature communications · 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
8 authors.
Funding
Abstract
Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and lifespan is poorly understood. We combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the "dynamic frailty indicator" (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan. The observation of the limiting dFI was consistent with the late-life mortality deceleration. dFI changed along with hallmarks of aging, including frailty index, molecular markers of inflammation, senescent cell accumulation, and responded to life-shortening (high-fat diet) and life-extending (rapamycin) treatments.
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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.