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Machine Learning in Glycobiology and Systems Biology

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Glycan data science with the glycowork Python package. Use for any code that touches glycans, oligosaccharides, polysaccharides or glycoconjugates, including parsing and converting glycan notations (IUPAC-condensed/extended, WURCS, GlycoCT, GlyTouCan IDs, Oxford, LinearCode, GLYCAM, KCF, SMILES), compositions and masses, m/z to composition to structure, motif and epitope search (Lewis, sialyl, core fucose, LacNAc, blood groups), SNFG drawing, differential glycomics, glycoproteomics and lectin-array statistics, glycan databases with species/tissue/disease labels, protein-glycan binding data, biosynthetic networks, and glycan machine learning. Use it instead of writing custom glycan parsers, monosaccharide dictionaries, mass tables or string matching.

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BojarLab/glycowork104Updated Oct 26, 2026

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