Small-Sample Learning for Next-Generation Human Health Risk Assessment: Harnessing AI, Exposome Data, and Systems Biology

Environ Sci Technol. 2025 Jan 2. doi: 10.1021/acs.est.4c11832. Online ahead of print.

Tianxiang Wu^{1

2

3}, Lu Zhao^{1

2

3}, Mengyuan Ren^{1

2

3}, Song He⁴, Le Zhang⁵, Mingliang Fang⁶, Bin Wang^{1

2

3

7

8}

¹ Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
² Institute of Reproductive and Child Health, School of Public Health, Peking University, Beijing 100191, P. R. China.
³ Key Laboratory of Reproductive Health, National Health and Family Planning Commission of PR China, Beijing 100191, China.
⁴ Department of Bioinformatics, Academy of Military Medical Sciences, Beijing 100850, China.
⁵ School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.
⁶ Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
⁷ Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China.
⁸ Laboratory for Earth Surface Processes, College of Urban and Environmental Science, Peking University, Beijing 100871, China.

No abstract available

Keywords: big data; exposome; multimodal learning; risk assessment; small-sample learning; systems biology; transfer learning.