weiba

ISBRA

DGCL

DGCL: a contrastive learning method for predicting cancer driver genes based on graph diffusion

HRLCDR

Hypergraph Representation Learning for Cancer Drug Response Prediction

HHMDA

Prediction of miRNA-disease association based on heterogeneous hypergraph convolution and heterogeneous graph multi-scale convolution

MHCLMDA

MHCLMDA:Multi-hypergraph contrastive learning for miRNA-disease association prediction

DAPL

Predicting clinical anticancer drug response of patient by using domain alignment and prototypical learning

HLMG

Hierarchical graph representation learning with multi-granularity features for anti-cancer drug response prediction

AIforCancerDriver

The platform for cancer driver detection based on AI methods

MTGCN

MVCLST

A multi-view comparative learning method for spatial transcriptomics data clustering

MCAGCN

An Explainable Method for Alzheimer’s Disease Diagnosis with Brain Imaging Genetic Data by Incorporating Multi-stream Attention Mechanisms and Graph Convolutional Networks

MOFGCN

MTIGCN

Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks

MNGCL

Multi-graph contrastive learning for cancer driver gene identification

MCRGCN

Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration

MRNGCN

Integrating multiple networks to identify cancer driver genes based on heterogeneous graph convolution with self-attention mechanism

NIGCNDriver

A graph convolution network-based model for prioritizing personalized cancer driver genes of individual patients

GM-GCN

The code and data for the paper "MiRNA-gene network embedding for predicting cancer driver genes"

NIHGCN

Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions

pDriverGCN

Source code of pDriverGCN for "Identification of personalized driver genes for individuals using graph convolution network"

HGCNMDA

Predicting miRNA-disease associations from miRNA-gene-disease heterogeneous network with multi-relational graph convolutional network model

Multi-ensem

The data and sourcecode of the paper entitled "Dai, Wei, Bingxi Chen, Wei Peng, Xia Li, Jiancheng Zhong, and Jianxin Wang. "A Novel Multi-Ensemble Method for Identifying Essential Proteins." Journal of Computational Biology 28, no. 7 (2021): 637-649."

TSGCNN

RLAG

RLAG

ERGCN

EntroRank

An entropy based method for identifying cancer driver genes

DDCMNMF

The source code of method for predicting cancer subtype

MDN-NMTF

Predicting miRNA-disease association based on modularity preserving heterogeneous network embedding

MVFA

the code of Multi-View Feature Aggregation for predicting microbe- disease association

Network-embedding

Network embedding protein-protein interaction network for human essential genes identification