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Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations.
Digit DiscovSorrentino S +7 more
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2021
Graphs are universal representations of pairwise relations with many real-world applications. In the healthcare domain, graphs are widely observed as relations of biomedical entities, including the graph structures of molecules, drug-drug interaction networks, protein-protein interaction networks, and gene expression networks and biomedical knowledge ...
Cao Xiao, Jimeng Sun
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Graphs are universal representations of pairwise relations with many real-world applications. In the healthcare domain, graphs are widely observed as relations of biomedical entities, including the graph structures of molecules, drug-drug interaction networks, protein-protein interaction networks, and gene expression networks and biomedical knowledge ...
Cao Xiao, Jimeng Sun
+6 more sources
Neural Networks Are Graphs! Graph Neural Networks for Equivariant Processing of Neural Networks
2023Neural networks that can process the parameters of other neural networks find applications in diverse domains, including processing implicit neural representations, domain adaptation of pretrained networks, generating neural network weights, and predicting generalization errors.
Zhang, D.W. +5 more
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Graph Neural Networks : Graph Representation Learning using Neural Networks
This book, "Graph Neural Networks: Graph Representation Learning with a Neural Network Approach," offers a comprehensive and up-to-date guide to the dynamic field of Graph Neural Networks (GNNs). It serves as an essential Persian-language resource for students, researchers, and AI practitioners.Boreiri, Zahra +2 more
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