Bi|Level Graph Neural Networks for Drug|Drug Interaction Prediction

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI).

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

We introduce BI-GNN (Bi-Level Graph Neural Networks) for modeling biological link prediction tasks such as drug- drug interaction (DDI) (Vilar et al., ...

codeKgu/BiLevel-Graph-Neural-Network - GitHub

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction. ICML 2020 Graph Representation Learning and Beyond (GRL+) Workshop ...

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

We introduce BI-GNN (Bi-Level Graph Neural Networks) for modeling biological link prediction tasks such as drug- drug interaction (DDI) ...

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

Bi-GNN is introduced for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI), which allows ...

Prediction of drug-drug interaction events using graph neural ...

The DANN-DDI model after constructing multiple drug feature networks adopts an attention neural network to aggregate the learned drug ...

A dual graph neural network for drug–drug interactions prediction ...

Therefore, we proposed a dual graph neural network named DGNN-DDI to learn drug molecular features by using molecular structure and interactions ...

Multi-type feature fusion based on graph neural network for drug ...

In this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the ...

Drug-drug interactions prediction based on deep learning and ...

In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available biomedical data and public ...

DDI-GCN: Drug-drug interaction prediction via explainable graph ...

Here, we introduce DDI-GCN, a method that utilizes graph convolutional networks (GCN) to predict DDIs based on chemical structures. We demonstrate that this ...

iNGNN-DTI: prediction of drug–target interaction with interpretable ...

The analysis is conducted on graph data representing drugs and targets by using a specific type of nested graph neural network, in which the ...

Multi-layer graph attention neural networks for accurate drug-target ...

In the crucial process of drug discovery and repurposing, precise prediction of drug-target interactions (DTIs) is paramount.

Graph neural network approaches for drug-target interactions

Therefore, the emerging graph neural network has been rapidly applied to predict DTIs, and proved effective in finding repositioning drugs and accelerating drug ...

Knowledge Graph Neural Network with Spatial-Aware Capsule for ...

Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction ... Abstract: Uncovering novel drug-drug ...

Deep learning for drug‐drug interaction prediction: A ...

Finally, the data of the drug pair is fed into a HOLE-based neural network interaction predictor to predict the presence of interactions between ...

BridgeDPI: a novel Graph Neural Network for predicting drug ...

Through information passing on this drug–protein association network, a Graph Neural Network can capture the network-level information among diverse drugs and ...

Predicting drug-drug interactions using heterogeneous graph neural ...

This research centers on predicting drug-drug interactions (DDIs) using a novel approach involving graph neural networks (GNNs) with integrated attention ...

DDI Prediction via Heterogeneous Graph Attention Networks

Information systems → Data mining; Computing platforms. KEYWORDS. Drug-drug Interaction, Link Prediction, Chemical Structure, Graph. Neural Network, ...

(PDF) Multi-type feature fusion based on graph neural network for ...

Conclusions Our proposed model can efficiently integrate the information from SMILES sequences, molecular graphs and drug-drug interaction networks. We find ...

A substructure‐aware graph neural network incorporating relation ...

Identifying drug–drug interactions (DDIs) is an important aspect of drug design research, and predicting DDIs serves as a crucial guarantee ...