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- W3204332977 abstract "Abstract RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein interactions for select proteins, however the time- and resource-intensive nature of these technologies call for the development of computational methods to complement their predictions. Here we leverage recent, large-scale CLIP-seq experiments to construct a de novo predictor of RNA-protein interactions based on graph neural networks (GNN). We show that the GNN method allows not only to predict missing links in an RNA-protein network, but to predict the entire complement of targets of previously unassayed proteins, and even to reconstruct the entire network of RNA-protein interactions in different conditions based on minimal information. Our results demonstrate the potential of modern machine learning methods to extract useful information on post-transcriptional regulation from large data sets." @default.
- W3204332977 created "2021-10-11" @default.
- W3204332977 creator A5017674487 @default.
- W3204332977 creator A5021681966 @default.
- W3204332977 date "2021-09-30" @default.
- W3204332977 modified "2023-10-17" @default.
- W3204332977 title "<i>De novo</i> prediction of RNA-protein interactions with Graph Neural Networks" @default.
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- W3204332977 doi "https://doi.org/10.1101/2021.09.28.462100" @default.
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