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生物医学数据的自主自我进化研究:DREAM范式。

Autonomous Self-Evolving Research on Biomedical Data: The DREAM Paradigm.

作者信息

Deng Luojia, Wu Yijie, Ren Yongyong, Lu Hui

机构信息

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.

SJTU-Yale Joint Center for Biostatistics and Data Science, Technical Center for Digital Medicine, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China.

出版信息

Adv Sci (Weinh). 2025 May 8:e2417066. doi: 10.1002/advs.202417066.

Abstract

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.

摘要

在当代生物医学研究中,数据驱动方法的效率受到大数据量、工具选择的复杂性以及人力资源有限的制约。为应对这些挑战,开发了一种数据驱动的自我进化自主系统(DREAM),这是首个能够在无人干预的情况下独立进行科学研究的完全自主生物医学研究系统。DREAM能自主制定和演化科学问题、配置计算环境,并进行结果评估与验证。与现有的半自主系统不同,DREAM无需人工干预即可运行,并在实际生物医学场景中得到了验证。它在问题生成方面超过了顶尖科学家的平均水平,在环境配置方面比经验丰富的人类研究人员成功率更高,还发现了新的科学发现。在弗雷明汉心脏研究中,它展示出的效率比普通科学家高出一万多倍。作为一个完全自主、自我进化的系统,DREAM为加速生物医学发现以及推动其他数据驱动的科学学科发展提供了一个强大而高效的解决方案。

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