Designing nanotheranostics with machine learning.

Saved in:
Bibliographic Details
Title: Designing nanotheranostics with machine learning.
Authors: Rao L; Shenzhen Bay Laboratory, Shenzhen, China. lrao@szbl.ac.cn., Yuan Y; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.; Department of Computer Science, Boston College, Chestnut Hill, MA, USA., Shen X; Tencent AI Lab, Shenzhen, China.; Intellindust, Shenzhen, China., Yu G; Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Department of Chemistry, Tsinghua University, Beijing, China., Chen X; Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and Faculty of Engineering, National University of Singapore, Singapore, Singapore. chen.shawn@nus.edu.sg.; Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. chen.shawn@nus.edu.sg.; Nanomedicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. chen.shawn@nus.edu.sg.; Theranostics Center of Excellence (TCE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. chen.shawn@nus.edu.sg.; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore. chen.shawn@nus.edu.sg.
Source: Nature nanotechnology [Nat Nanotechnol] 2024 Dec; Vol. 19 (12), pp. 1769-1781. Date of Electronic Publication: 2024 Oct 03.
Publication Type: Journal Article; Review
Journal Info: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101283273 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1748-3395 (Electronic) Linking ISSN: 17483387 NLM ISO Abbreviation: Nat Nanotechnol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1748-3395
DOI:10.1038/s41565-024-01753-8