Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics

Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of me...

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Detalles Bibliográficos
Autores principales: Lenski, Marie, Maallem, Saïd, Zarcone, Gianni, Garçon, Guillaume, Lo-Guidice, Jean-Marc, Anthérieu, Sébastien, Allorge, Delphine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962007/
https://www.ncbi.nlm.nih.gov/pubmed/36837901
http://dx.doi.org/10.3390/metabo13020282

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962007/
https://www.ncbi.nlm.nih.gov/pubmed/36837901
http://dx.doi.org/10.3390/metabo13020282

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