Graph convolutional neural networks for analyzing glycans [LEGACY; use glycowork implementation]
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Updated
Mar 9, 2021 - Jupyter Notebook
Graph convolutional neural networks for analyzing glycans [LEGACY; use glycowork implementation]
Deep learning model to predict interactions between proteins and glycans [LEGACY; use glycowork implementation]
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 scripts for analysis of DI-, GC- and LC-MS data
Script for quantification of glucosaminoglycans (GAGs) based on Proteome Discoverer output, with web-interface powered by Bokeh
R package for annotation of glycans in MS1 and MS2 data
Predicting single-cell glycosylation features from scRNA-seq
A glycogene ontology describing the functions they perform and their involvement in glycosylation pathways
GNN model to learn latent space representations of glycans on an atomic level
This repository includes the codes used for channel capacity calculation present in Fuchsberger et al. paper.
Community-curated list of software packages and data resources for Glycoscience
R package for calculation and mass spectrometry annotation of glycans
A Python framework for the rapid modeling of glycans
Package for processing and analyzing glycans and their role in biology.
GlyCombo, a combinatorial glycan composition assignment tool
Glycan Informed Foundational Framework for Learning Abstract Representations, based on Combinatorial Complexes and Heterogeneous GNNs
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