ROASMI: accelerating small molecule identification by repurposing retention data

The limited replicability of retention data hinders its application in untargeted metabolomics for small molecule identification. While retention order models hold promise in addressing this issue, their predictive reliability is limited by uncertain generalizability. Here, we present the ROASMI mod...

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Veröffentlicht in:	Journal of cheminformatics Jg. 17; H. 1; S. 20 - 15
Hauptverfasser:	Sun, Fang-Yuan, Yin, Ying-Hao, Liu, Hui-Jun, Shen, Lu-Na, Kang, Xiu-Lin, Xin, Gui-Zhong, Liu, Li-Fang, Zheng, Jia-Yi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 14.02.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Analysis Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Coupling (molecular) Datasets Deep learning Documentation and Information in Chemistry Improving Reproducibility and Reusability in the Journal of Cheminformatics Isomers Liquid chromatography Metabolomics Molecular modelling Prediction models Predictions Replicability Retention Retention order Sequences Small-molecule identification Theoretical and Computational Chemistry Mississippi Deep learning Metabolomics Small-molecule identification Retention order Replicability
ISSN:	1758-2946, 1758-2946
Online-Zugang:	Volltext
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