Results 231 to 240 of about 437,099 (249)

Graph Neural Network-Based Toxicity Prediction by Integrating Molecular Fingerprints and Knowledge Graph Features. [PDF]

Toxics
Xie J, Liu W, Hu W, Ouyang M, Huang T.
europepmc   +1 more source

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset. [PDF]

Bioinformatics
Lyu R, Vannan A, Kropski JA, Banovich NE, McCarthy DJ.   +4 more
europepmc   +1 more source

Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations.

Digit Discov
Sorrentino S, Gussoni A, Calcagno F, Pasotti G, Avagliano D, Rivalta I, Garavelli M, Polli D.   +7 more
europepmc   +1 more source

Graph Neural Network-Based Multi-Scale Whole Slide Image Fusion for pT Staging of Muscle-Invasive Bladder Cancer. [PDF]

Cancer Sci
Li Q, Chen QF, Liao NQ.
europepmc   +1 more source

Graph neural network and diffusion model for modeling RNA interatomic interactions. [PDF]

Bioinformatics
Justyna M, Zirbel C, Antczak M, Szachniuk M.   +3 more
europepmc   +1 more source

Correction to: GraphAge: Unleashing the power of graph neural network to decode epigenetic aging. [PDF]

PNAS Nexus
europepmc   +1 more source

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Graph Neural Networks

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
  +6 more sources

Neural Networks Are Graphs! Graph Neural Networks for Equivariant Processing of Neural Networks

2023
Neural 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., Kofinas, M., Zhang, Y., Chen, Y., Burghouts, G.J., Snoek, C.G.M.   +5 more
openaire   +1 more source

Graph Ensemble Neural Network

Information Fusion, 2023
Rui Duan, Chungang Yan, Junli Wang, Changjun Jiang   +3 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, Moeini, Ali, Abedian, Rooholah   +2 more
openaire   +1 more source