Liquid-chromatography retention order prediction for metabolite identification | Zendy

Eric Bach | Zendy; Sándor Szedmák | Zendy; Céline Brouard | Zendy; Sebastian Böcker | Zendy; Juho Rousu | Zendy

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Liquid-chromatography retention order prediction for metabolite identification

Author(s) -

Eric Bach,

Sándor Szedmák,

Céline Brouard,

Sebastian Böcker,

Juho Rousu

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty590

Subject(s) - metabolite , chromatography , identification (biology) , retention time , chemistry , high performance liquid chromatography , computer science , biology , biochemistry , botany

Liquid Chromatography (LC) followed by tandem Mass Spectrometry (MS/MS) is one of the predominant methods for metabolite identification. In recent years, machine learning has started to transform the analysis of tandem mass spectra and the identification of small molecules. In contrast, LC data is rarely used to improve metabolite identification, despite numerous published methods for retention time prediction using machine learning.

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