Glycan Informed Foundational Framework for Learning Abstract Representations, based on Combinatorial Complexes and Heterogeneous GNNs
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Updated
Oct 25, 2024 - Python
Glycan Informed Foundational Framework for Learning Abstract Representations, based on Combinatorial Complexes and Heterogeneous GNNs
GlyCombo, a combinatorial glycan composition assignment tool
Package for processing and analyzing glycans and their role in biology.
A Python framework for the rapid modeling of glycans
R package for calculation and mass spectrometry annotation of glycans
Community-curated list of software packages and data resources for Glycoscience
This repository includes the codes used for channel capacity calculation present in Fuchsberger et al. paper.
GNN model to learn latent space representations of glycans on an atomic level
A glycogene ontology describing the functions they perform and their involvement in glycosylation pathways
Predicting single-cell glycosylation features from scRNA-seq
R package for annotation of glycans in MS1 and MS2 data
Script for quantification of glucosaminoglycans (GAGs) based on Proteome Discoverer output, with web-interface powered by Bokeh
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.
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]
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