BiologyScientific Reports
SkipGNN: predicting molecular interactions with skip-graph networks
K. Huang, C. Xiao, et al.
Discover SkipGNN, a groundbreaking graph neural network designed by Kexin Huang and colleagues to enhance molecular interaction predictions by utilizing not just direct connections but also second-order similarities. This innovative approach improves the model's robustness, especially with noisy data, while generating meaningful biological embeddings.
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