Graph Convolutional Neural Networks to Analyze Complex Carbohydrates

Graph convolutional neural networks (GCNs) have attracted increasing amounts of attention over the last couple of years, with more and more disciplines finding use for them. This has also been extended into the life sciences, as GCNs have been used to analyze proteins, drugs, and of course biological networks. One key advantage of GCNs that has enabled this expansion is their ability to natively work with nonlinear data formats, in contrast to more linear data structures such as in natural languages. Because of this feature, we also implemented GCNs for our own topic of interest, the study of complex carbohydrates or glycans. Glycans are ubiquitous in biology, decorating every cell and playing key roles in processes such as viral infection or tumor immune evasion.