Integrating machine learning and metabolomics to identify PFAS-associated metabolic alterations related to lung cancer risk.
- DOI
- 10.1016/j.envint.2026.110276
- Published
- 2026 May
- Container
- Environment international
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.envint.2026.110276,
title = {Integrating machine learning and metabolomics to identify PFAS-associated metabolic alterations related to lung cancer risk.},
author = {Qu J and Zhang X and Jin Q and Guo Y and Yu H and Zhan G and Mao W and Xiang S and Zhao M and Jin H},
year = {2026},
journal = {Environment international},
doi = {10.1016/j.envint.2026.110276},
url = {https://doi.org/10.1016/j.envint.2026.110276}
}RIS
TY - JOUR TI - Integrating machine learning and metabolomics to identify PFAS-associated metabolic alterations related to lung cancer risk. AU - Qu J AU - Zhang X AU - Jin Q AU - Guo Y AU - Yu H AU - Zhan G AU - Mao W AU - Xiang S AU - Zhao M AU - Jin H PY - 2026 JO - Environment international DO - 10.1016/j.envint.2026.110276 UR - https://doi.org/10.1016/j.envint.2026.110276 ER -
APA
J, Q., X, Z., Q, J., Y, G., H, Y., G, Z., W, M., S, X., M, Z., & H, J. (2026). Integrating machine learning and metabolomics to identify PFAS-associated metabolic alterations related to lung cancer risk.. Environment international. https://doi.org/10.1016/j.envint.2026.110276
Source records
- pubmed · retrieved 2026-09-25T07:25:06.420Z