  In contemporary biomedical research, the efficiency of data-driven approaches
is hindered by large data volumes, tool selection complexity, and human
resource limitations, necessitating the development of fully autonomous
research systems to meet complex analytical needs. Such a system should include
the ability to autonomously generate research questions, write analytical code,
configure the computational environment, judge and interpret the results, and
iteratively generate in-depth questions or solutions, all without human
intervention. Here we developed DREAM, the first biomedical Data-dRiven
self-Evolving Autonomous systeM, which can independently conduct scientific
research without human involvement. Utilizing a clinical dataset and two omics
datasets, DREAM demonstrated its ability to raise and deepen scientific
questions, with difficulty scores for clinical data questions surpassing top
published articles by 5.7% and outperforming GPT-4 and bioinformatics graduate
students by 58.6% and 56.0%, respectively. Overall, DREAM has a success rate of
80% in autonomous clinical data mining. Certainly, human can participate in
different steps of DREAM to achieve more personalized goals. After evolution,
10% of the questions exceeded the average scores of top published article
questions on originality and complexity. In the autonomous environment
configuration of the eight bioinformatics workflows, DREAM exhibited an 88%
success rate, whereas GPT-4 failed to configure any workflows. In clinical
dataset, DREAM was over 10,000 times more efficient than the average scientist
with a single computer core, and capable of revealing new discoveries. As a
self-evolving autonomous research system, DREAM provides an efficient and
reliable solution for future biomedical research. This paradigm may also have a
revolutionary impact on other data-driven scientific research fields.
