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A novel method for drug-target interaction prediction based on …
WEBNov 3, 2022 · We propose a novel drug-target prediction model based on graph transformer network (DTI-GTN) in this paper. Firstly, we use seven different level relationships of drugs and targets to construct features of drug-target interaction with jaccard similarity and random walk with restart method.
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A network integration approach for drug-target interaction
WEBSep 18, 2017 · DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and...
DTi2Vec: Drug–target interaction prediction using network …
WEBSep 22, 2021 · DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique.
Diffusion Tensor Imaging (DTI) Explained - News-Medical.net
WEBOct 11, 2018 · Diffusion tensor imaging (DTI) is a newly developed magnetic resonance imaging (MRI) technique that analyzes the anatomy of nerve cells and a complex neuronal network of the brain.
GitHub - luoyunan/DTINet: A Network Integration Approach for …
WEBDTINet is a computational pipeline to predict novel drug-target interactions (DTIs) from heterogeneous network. DTINet focuses on learning a low-dimensional vector representation of features for each node in the heterogeneous network, and then predicts the likelihood of a new DTI based on these representations via a …
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DTI-HeNE: a novel method for drug-target interaction ... - PubMed
WEBSep 3, 2021 · Results: We propose a DTI prediction method DTI-HeNE (DTI based on Heterogeneous Network Embedding), which is specifically designed to cope with the bipartite DTI relations for generating high-quality embeddings of drug-target pairs.
Predicting drug–target binding affinity with cross-scale graph ...
WEBJan 13, 2024 · The proposed method specifically extracts network-scale features of drugs and proteins from the DTI network, as well as molecule-scale features of drugs and proteins from the drug and target molecule network, via graph neural networks.