ROASMI: accelerating small molecule identification by repurposing retention data

Abstract 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 R...

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Principais autores:	Fang-Yuan Sun, Ying-Hao Yin, Hui-Jun Liu, Lu-Na Shen, Xiu-Lin Kang, Gui-Zhong Xin, Li-Fang Liu, Jia-Yi Zheng
格式:	Artigo
語言:	Inglês
出版:	BMC 2025-02-01
叢編:	Journal of Cheminformatics
主題:	Metabolomics Retention order Small-molecule identification Replicability Deep learning
在線閱讀:	https://doi.org/10.1186/s13321-025-00968-8
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