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.

Content Type:
Paper
Document Number:
PO-CON1857E
Product Type:
Liquid Chromatograph-Mass Spectrometry, Mass Spectrometry
Keywords:
deep learning algorithm, Pharmaceutical, Life Science, Metabolomics
Language:
English
File Name:
jpo119030.pdf
File Size:
561kb

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