Supervised Machine Learning and Graph Neural Networks to Predict Collision Cross-Section Values of Aquatic Dissolved Organic Compounds.
- DOI
- 10.1021/jasms.5c00276
- Published
- 2026-01-08
- Container
- J Am Soc Mass Spectrom
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1021/jasms.5c00276,
title = {Supervised Machine Learning and Graph Neural Networks to Predict Collision Cross-Section Values of Aquatic Dissolved Organic Compounds.},
author = {Ebrahimi S and Criqui L and Soldera A and Guéguen C.},
year = {2026},
journal = {J Am Soc Mass Spectrom},
doi = {10.1021/jasms.5c00276},
url = {https://doi.org/10.1021/jasms.5c00276}
}RIS
TY - JOUR TI - Supervised Machine Learning and Graph Neural Networks to Predict Collision Cross-Section Values of Aquatic Dissolved Organic Compounds. AU - Ebrahimi S AU - Criqui L AU - Soldera A AU - Guéguen C. PY - 2026 JO - J Am Soc Mass Spectrom DO - 10.1021/jasms.5c00276 UR - https://doi.org/10.1021/jasms.5c00276 ER -
APA
S, E., L, C., A, S., & C., G. (2026). Supervised Machine Learning and Graph Neural Networks to Predict Collision Cross-Section Values of Aquatic Dissolved Organic Compounds.. J Am Soc Mass Spectrom. https://doi.org/10.1021/jasms.5c00276
Source records
- europe-pmc · retrieved 2026-09-26T14:54:47.696Z