GNN model to learn latent space representations of glycans on an atomic level
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Updated
Nov 24, 2022 - Python
GNN model to learn latent space representations of glycans on an atomic level
R package for calculation and mass spectrometry annotation of glycans
R scripts for analysis of DI-, GC- and LC-MS data
Command line executable tool to calculate all possible glycan molecules and the m/z values of their ions given a set up input parameters.
R package for annotation of glycans in MS1 and MS2 data
This repository includes the codes used for channel capacity calculation present in Fuchsberger et al. paper.
Glycan Informed Foundational Framework for Learning Abstract Representations, based on Combinatorial Complexes and Heterogeneous GNNs
Community-curated list of software packages and data resources for Glycoscience
Script for quantification of glucosaminoglycans (GAGs) based on Proteome Discoverer output, with web-interface powered by Bokeh
A Python framework for the rapid modeling of glycans
GlyCombo, a combinatorial glycan composition assignment tool
A glycogene ontology describing the functions they perform and their involvement in glycosylation pathways
Predicting single-cell glycosylation features from scRNA-seq
Deep learning model to predict interactions between proteins and glycans [LEGACY; use glycowork implementation]
Graph convolutional neural networks for analyzing glycans [LEGACY; use glycowork implementation]
Package for processing and analyzing glycans and their role in biology.
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