Molecule-dynamic-based Aging Clock and Aging Roadmap Forecast with Sundial

2025-01-04

AI Summary

Traditional supervised molecular aging clocks suffer from bias due to label dependency and strong distributional assumptions. To address this, we propose Sundial, an unsupervised molecular aging clock. Methodologically, Sundial introduces diffusion field modeling into molecular dynamics representation for the first time, enabling unbiased biological age estimation and personalized aging trajectory prediction via unsupervised ordinal learning that disentangles population-level aging patterns from individual heterogeneity. Technically, it integrates multi-scale trajectory modeling with diffusion-based generative mechanisms, overcoming limitations of supervised models—including reliance on labeled chronological age and restrictive distributional assumptions. Experiments demonstrate that Sundial significantly improves disease risk stratification, accurately identifies subpopulations with accelerated aging, and enables age- and sex-specific analyses as well as characterization of healthy aging trajectories. By eliminating supervision-induced bias and grounding inference in mechanistic biophysical dynamics, Sundial establishes a new paradigm for mechanism-driven aging research.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Addressing the unavoidable bias inherent in supervised aging clocks, we introduce Sundial, a novel framework that models molecular dynamics through a diffusion field, capturing both the population-level aging process and the individual-level relative aging order. Sundial enables unbiasedestimation of biological age and the forecast of aging roadmap. Fasteraging individuals from Sundial exhibit a higher disease risk compared to those identified from supervised aging clocks. This framework opens new avenues for exploring key topics, including age- and sex-specific aging dynamics and faster yet healthy aging paths.

Problem

• Ageing Measurement Bias
• Biological Age Determination
• Healthspan Prediction

Innovation

• Sundial
• Biological Age Measurement
• Personalized Aging Prediction