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• W2537215459 abstract "Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92 % of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0." @default.
• W2537215459 created "2016-10-28" @default.
• W2537215459 creator A5038786156 @default.
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• W2537215459 date "2016-10-20" @default.
• W2537215459 modified "2023-10-14" @default.
• W2537215459 title "Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data" @default.
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• W2537215459 doi "https://doi.org/10.3390/metabo6040037" @default.
• W2537215459 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5192443" @default.
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