Predicting glycan structure from tandem mass spectrometry via deep learning

Curated set of LC-MS/MS data from glycomics studies. Used for training and applying CandyCrunch, a deep learning model to predict glycan structure from LC-MS/MS data, described in Urban et al., Nat Methods, 2024 and https://github.com/BojarLab/CandyCrunch. Files: full_dataset.xlsx: Full dataset with all annotated LC-MS/MS glycan spectra X_train.pkl: spectra and metadata from our training set y_train.pkl: labels from our training set X_test.pkl: spectra and metadata from our independent test set y_test.pkl: labels from our independent test set glycans.pkl: glycans in IUPAC-condensed nomenclature in the same order as the label-encoding

Related Organizations

University of Gothenburg
Sweden
University of Southampton
United Kingdom
Maynooth University
Ireland

Keywords

glycan, machine learning, carbohydrate, deep learning, bioinformatics, mass spectrometry

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