GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data

In this study, we used a Bayesian deep neural network model to automatically identify and classify vibrational fingerprints of several monosaccharides. We report high performances of the obtained trained algorithm (GlAIcomics), that can be used to discriminate contamination and identify a molecule with a high degree of confidence. It opens the possibility to use artificial intelligence in combination with spectroscopy-augmented mass spectrometry for carbohydrates sequencing and glycomics applications. Beilstein J. Org. Chem. 2023, 19, 1825–1831. doi:10.3762/bjoc.19.134
Source: Beilstein Journal of Organic Chemistry - Category: Chemistry Authors: Tags: Bayesian neural network deep learning glycomics IR spectroscopy Full Research Paper Source Type: research