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drwbncf

A weighted bilinear neural collaborative filtering approach for drug repositioning

dti-cnn

A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

dti-gat

Drug-Target Interaction Prediction with GraphAttention networks

dtinet-with-python

Realizing DTINet with python

egat

a pytorch implementation of Exploiting Edge Features in Graph Neural Networks.

emergnn

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network. Nature Computational Science. 2023.

equivs

ffgt

Hybrid Focal and Full-Range Attention Based Graph Transformers

flatgcn

This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation).

gadti

gamclda

The implementation of “Inferring LncRNA-disease Associations Based On Graph Autoencoder Matrix Completion”, Ximin Wu,Wei Lan,Qingfeng Chen,Yi Dong,Wei Peng,Jin Liu,Jianxin Wang

gamlp

ganlda

The implementation of “GANLDA: Graph attention network for lncRNA-disease associations prediction”

gat-pytorch

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

gatcl2cd

Predicting CircRNA-Disease associations via feature convolution learning with heterogeneous graph attention network

gatecda

This code and data were provided for the paper "Predicting CircRNA-Drug Sensitivity Associations via Graph Attention Auto-Encoder"

gatmda

Graph attention networks-based framework for human microbe-disease association prediction

gcae

graph convolutional auto-encoder

gde

SIGIR 2022 Paper: Less is More: Reweighting Important Spectral Graph Features for Recommendation

genghao-lightning

GengHao Lightning！

genghao-toolkit

GengHao Toolkit，深度学习工作者的一大利器！（雾）

ggatlda

This is about lncRNA-disease-drug prediction in bioinformatics

gipa

GIPA：a general information propagation algorithm for graph learning

gnn-magna

Multi-hop Attention Graph Neural Network

graph2mda

Graph2MDA is an architecture based on the multi-mode attributed graph for identifing new microbe-drug association.

graph_nets

PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

graph_transformer_networks

Graph Transformer Networks (Authors' PyTorch implementation for the NeurIPS 19 paper)

graphcda

GraphCDA: a multi-modal graph representation learning framework for predicting circRNA-disease associations

graphhinge

Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

graphmae

Code for KDD'22 paper "GraphMAE: Self-supervised Masked Graph Autoencoders.