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