Deep learning methods applied to the analysis of metabolomics data

A number of machine learning methods have been applied to bioinformatics and metabolite analyses including self-organizing maps, support vector machines, kernel machines, Bayesian networks or fuzzy logic. Advanced machine learning algorithms have been also applied to in silico MS chromatogram annotation for metabolite identification. As a general approach in peak integration algorithms are designed to determine the start point and end point of a chromatographic peak to enable a calculation of peak area.

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Liquid Chromatograph-Mass Spectrometry, Mass Spectrometry
deep learning algorithm, Pharmaceutical, Life Science, Metabolomics
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