journal article·2025-06Open access

Autonomous Self‐Evolving Research on Biomedical Data: The DREAM Paradigm

Luojia Deng, Yijie Wu, Yongyong Ren, Hui Lu

Advanced Science· Volume 12· Issue 22

Abstract

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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Gene expression and cancer classification · Single-cell and spatial transcriptomics · Genomics and Phylogenetic Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

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

Funding

Science and Technology Commission of Shanghai Municipality

References (70)

Show source-provided references
1. 10.1016/j.drudis.2013.10.01210.1016/j.drudis.2013.10.012 ↗
2. 10.1038/s41596-024-00986-010.1038/s41596-024-00986-0 ↗
3. Kaddour J. · arXiv · 2023
4. Chowdhery A. · J. Mach. Learn. Res. · 2023 · 24 · 1
5. 10.1038/s41591-023-02448-810.1038/s41591-023-02448-8 ↗
6. OpenAI · arXiv · 2023
7. 10.1038/s41586-023-06792-010.1038/s41586-023-06792-0 ↗
8. 10.1038/s42256-024-00832-810.1038/s42256-024-00832-8 ↗
9. 10.1002/adma.20241352310.1002/adma.202413523 ↗
10. Xin Q. · bioRxiv · 2024
11. Liu Y. · bioRxiv · 2024
12. 10.1038/s41467-024-45879-810.1038/s41467-024-45879-8 ↗
13. 10.1056/AIoa240055510.1056/AIoa2400555 ↗
14. Guo S. · arXiv · 2024
15. Huang Q. · arXiv · 2024
16. Ren Y. · bioRxiv · 2024
17. Baek J. · arXiv · 2024
18. 10.1016/j.jmat.2023.08.00710.1016/j.jmat.2023.08.007 ↗
19. 10.1093/gigascience/giaa14010.1093/gigascience/giaa140 ↗
20. 10.1038/s42003-022-03397-710.1038/s42003-022-03397-7 ↗
21. Waldmann J. · BMC Res. Notes · 2014 · 7, 365
22. 10.1101/gr.107524.11010.1101/gr.107524.110 ↗
23. 10.1093/bioinformatics/btp39410.1093/bioinformatics/btp394 ↗
24. 10.1371/journal.pcbi.100575210.1371/journal.pcbi.1005752 ↗
25. 10.1038/s41587-019-0201-410.1038/s41587-019-0201-4 ↗
26. 10.1186/s13059-014-0550-810.1186/s13059-014-0550-8 ↗
27. 10.1086/51979510.1086/519795 ↗
28. Sjolander A. · ivtools: Instrumental Variables · 2024
29. 10.1093/bioinformatics/btt68410.1093/bioinformatics/btt684 ↗
30. 10.1038/ncomms1404910.1038/ncomms14049 ↗
31. Bradski G. · Dobb's J. Software Tools · 2000 · 120 · 122
32. 10.1007/s11263-007-0090-810.1007/s11263-007-0090-8 ↗
33. Cleary J. G. · bioRxiv · 2015
34. 10.1038/nbt.423510.1038/nbt.4235 ↗
35. Framingham heart study2024 https://www.framinghamheartstudy.org/(accessed: August 2024).
36. CDC 2022 Annual Survey Data2024 https://www.cdc.gov/brfss/annual_data/annual_2022.html(accessed: August 2024).
37. 10.1186/s12911-020-1023-510.1186/s12911-020-1023-5 ↗
38. 10.1016/0002-9149(89)90524-910.1016/0002-9149(89)90524-9 ↗
39. Lalo R. · Int. J. Environ. Res. Public Health · 2023 · 20, 3693
40. 10.1136/bmjopen-2018-02407010.1136/bmjopen-2018-024070 ↗
41. 10.1161/01.STR.0000221702.75002.6610.1161/01.STR.0000221702.75002.66 ↗
42. 10.1093/eurpub/ckt07310.1093/eurpub/ckt073 ↗
43. 10.1016/S0735-1097(85)80551-910.1016/S0735-1097(85)80551-9 ↗
44. 10.1093/eurheartj/ehm19810.1093/eurheartj/ehm198 ↗
45. 10.1038/s41576-024-00709-x10.1038/s41576-024-00709-x ↗
46. 10.1038/nbt148610.1038/nbt1486 ↗
47. 10.1056/NEJMoa130655510.1056/NEJMoa1306555 ↗
48. 10.1093/bioinformatics/btu17010.1093/bioinformatics/btu170 ↗
49. Li H. · arXiv · 2013
50. 10.1093/gigascience/giab00810.1093/gigascience/giab008 ↗
51. 10.1093/bioinformatics/btad51710.1093/bioinformatics/btad517 ↗
52. 10.1186/s13059-016-0881-810.1186/s13059-016-0881-8 ↗
53. 10.1038/nrg174910.1038/nrg1749 ↗
54. 10.1111/wrr.1306810.1111/wrr.13068 ↗
55. 10.1093/nar/gkv00710.1093/nar/gkv007 ↗
56. Wu T. · Innovation · 2021 · 2
57. Wickham H. · 2016
58. 10.1016/0197-2456(95)00065-810.1016/0197-2456(95)00065-8 ↗
59. W.Wolberg O.Mangasarian N.Street W.Street Breast Cancer Wisconsin (Diagnostic) UCI Machine Learning Repository 1993 https://doi.org/10.24432/C5DW2B.
60. 10.1002/adfm.20240953110.1002/adfm.202409531 ↗
61. 10.1038/s41467-023-44141-x10.1038/s41467-023-44141-x ↗
62. 10.1016/j.media.2023.10304010.1016/j.media.2023.103040 ↗
63. Zhou X. · arXiv · 2025
64. 10.1038/s41467-024-44906-y10.1038/s41467-024-44906-y ↗
65. Sung Y. L. · arXiv · 2023
66. Goddard C. · arXiv · 2025
67. 10.1002/advs.20230672410.1002/advs.202306724 ↗
68. Wei J. · Adv. Neural Inf. Process. Syst. · 2022 · 35
69. 10.2105/AJPH.41.3.27910.2105/AJPH.41.3.279 ↗
70. 10.1371/journal.pone.026536810.1371/journal.pone.0265368 ↗