Article Versions Notes

Lenski M, Maallem S, Zarcone G, Garçon G, Lo-Guidice J-M, Anthérieu S, Allorge D. Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics. Metabolites. 2023; 13(2):282. https://doi.org/10.3390/metabo13020282

Lenski, Marie, Saïd Maallem, Gianni Zarcone, Guillaume Garçon, Jean-Marc Lo-Guidice, Sébastien Anthérieu, and Delphine Allorge. 2023. "Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics" Metabolites 13, no. 2: 282. https://doi.org/10.3390/metabo13020282

Lenski, M., Maallem, S., Zarcone, G., Garçon, G., Lo-Guidice, J.-M., Anthérieu, S., & Allorge, D. (2023). Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics. Metabolites, 13(2), 282. https://doi.org/10.3390/metabo13020282