DoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery

2026-09-28

AI Summary

This study addresses the challenges of discovering biomedical causal mechanisms, integrating multi-source evidence, and achieving interpretable predictions by proposing a self-evolving closed-loop framework based on a "hypothesis-test-refine" paradigm. This framework integrates large-scale multimodal data with literature-derived evidence to unify causal discovery, population-level validation, and interpretable prediction, thereby enabling autonomous hypothesis generation and iterative verification. Evaluated across more than two thousand problems, the proposed method yields statistically significant support within the vascular-metabolic system, facilitating precise causal attribution and mediation effect analysis. Ultimately, this work establishes a novel paradigm for exploring complex biomedical mechanisms.

Abstract

We introduce DoAtlas-2, a foundation for self-evolving causal biomedical discovery that organizes knowledge around causal mechanisms and advances through external evidence from human populations. DoAtlas-2 integrates 771 research resources covering more than 720,000 participants in 48 countries, from longitudinal clinical phenotypes, medical imaging, and continuous physiological signals to eight molecular layers, together with an evidence network of approximately 4.7 million literature-derived records over 93,566 concepts and 149,383 candidate causal relations. DoAtlas-2 autonomously formulates research questions from evidence gaps and unresolved mechanisms, prespecifies their causal designs, and generates validated analyses. Supporting, challenging, and unresolved results continuously revise mechanistic interpretations, the causal evidence state, and the discovery frontier, so that DoAtlas-2 self-evolves within a closed loop of hypothesis generation, empirical testing, and renewed discovery. DoAtlas-2 has systematically evaluated 2,031 research questions. In the Human Phenotype Project (HPP), it formulated 4,014 candidate pathway questions across vascular, early-glycemic, and hepatic-metabolic systems, and screening of the first 1,079 yielded statistical support for 756. Representative studies identify blood pressure as a convergence node linking adiposity, hepatic, and lipid phenotypes to vascular outcomes, and show that an adiposity-inflammation-blood-pressure pathway is largely attenuated by joint adjustment for body mass index (BMI) and smoking. The discovered vascular network constitutes a completely interpretable predictive foundation, admitting exact attribution of every prediction and closed-form mediation effects. DoAtlas-2 thereby unifies causal mechanism discovery, population-evidence testing, and interpretable prediction within one continuously evolving foundation.

Problem

• causal discovery
• biomedical knowledge
• self-evolving system
• evidence gaps
• interpretable prediction

Innovation

• Causal Discovery
• Self-Evolving System
• Multi-modal Integration
• Interpretable Prediction
• Biomedical Foundation