DoAtlas-2 builds self-learning platform for biomedical causal discovery
DoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery
Artificial Intelligence
Summary
Understanding what causes diseases or health changes is challenging. The authors created DoAtlas-2, a system that collects lots of biomedical data and medical studies to automatically ask questions about causes, plan how to test them, and learn from results. This system continuously updates its knowledge and predictions, improving how we understand health and disease mechanisms over time. It showed that blood pressure links body fat and liver functions to blood vessel health, providing clear and interpretable predictions.
What this means in practice
- •For clinical data scientists: Use DoAtlas-2 to generate and test causal hypotheses across multi-layer biomedical data for improved disease mechanism insights.
- •For pharmaceutical analysts: Employ DoAtlas-2’s interpretable causal networks to identify pathway targets linking phenotypes and inform drug discovery strategies.
Authors
Yulong Li, Rong Xia, Yuxuan Zhang, Jianxu Chen, Xiwei Liu, Haochen Xue, Maosheng Li, Yuhang Liu, Yibo Yuan, Yutong Xie, Chong Li, Jionglong Su, Hagai Rossman, Eran Segal, Imran Razzak
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.