Autonomous Self‐Evolving Research on Biomedical Data: The DREAM Paradigm
In contemporary biomedical research, the efficiency of data-driven methodologies is constrained by large data volumes, the complexity of tool selection, and limited human resources. To address these challenges, a Data-dRiven self-Evolving Autonomous systeM (DREAM) is developed as the first fully autonomous biomedical research system capable of independently conducting scientific investigations without human intervention. DREAM autonomously formulates and evolves scientific questions, configures computational environments, and performs result evaluation and validation. Unlike existing semi-autonomous systems, DREAM operates without manual intervention and is validated in real-world biomedical scenarios. It exceeds the average performance of top scientists in question generation, achieves a higher success rate in environment configuration than experienced human researchers, and uncovers novel scientific findings. In the context of the Framingham Heart Study, it demonstrated an efficiency that is over 10 000 times greater than that of average scientists. As a fully autonomous, self-evolving system, DREAM offers a robust and efficient solution for accelerating biomedical discovery and advancing other data-driven scientific disciplines.
Authors
- Yijie Wu
- Yongyong Ren (ORCID: https://orcid.org/0000-0001-9217-3483)
- Luojia Deng (ORCID: https://orcid.org/0000-0002-5321-3734)
- Hui Juan Lu (ORCID: https://orcid.org/0000-0001-8347-0830)
Institutions
- Shanghai Jiao Tong University (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1002/advs.202417066
- Citations
- 3
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 2.42
Funders
- Science and Technology Commission of Shanghai Municipality
- Shanghai Jiao Tong University
- Hong Kong University of Science and Technology
- Centre Scientifique et Technique du Bâtiment