Using graph convolutional neural networks to learn a representation for glycans.

ABSTRACT: As the only nonlinear and the most diverse biological sequence, glycans offer substantial challenges for computational biology. These carbohydrates participate in nearly all biological processes-from protein folding to viral cell entry-yet are still not well understood. There are few computational methods to link glycan sequences to functions, and they do not fully leverage all available information about glycans. SweetNet is a graph convolutional neural network that uses graph representation learning to facilitate a computational understanding of glycobiology. SweetNet explicitly incorporates the nonlinear nature of glycans and establishes a framework to map any glycan sequence to a representation. We show that SweetNet outperforms other computational methods in predicting glycan properti

SUBMITTER: Burkholz R 

PROVIDER: S-EPMC9208909 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature