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This article discusses the use of graph neural networks (GNNs) in representing biological data and their applications in predicting isoform function, RBP binding sites, gene coexpression, and gene interaction networks. It also reviews successful deep learning models for RNA biology and suggests good practices for designing and training effective models.
