News

Frontiers1y
Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network
Here, we developed an MDA prediction method called GPUDMDA by combining feature extraction based on graph attention autoencoder (GATE), reliable negative MDA selection based on positive-unlabeled (PU) ...
GitHub1y
scVAG: Unified Single-Cell Clustering via Variational-Autoencoder Integration with Graph Attention Autoencoder
scVAG is an innovative framework that integrates Variational Autoencoder (VAE) and Graph Attention Autoencoder (GATE) models for enhanced analysis of single-cell gene expression data. Built upon the ...
IEEE2y
Deep Self-Supervised Graph Attention Convolution Autoencoder for Networks Clustering
To solve this problem, we propose a Deep Self-Supervised Attention Convolution Autoencoder Graph Clustering (DSAGC) model and use it for social networks clustering. We divide the proposed model into ...
Frontiers2y
Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
The feature autoencoder builds and prunes the cell graph through learned embedding. The graph autoencoder adds the graph attention mechanism. It takes the constructed cell graph as input by adding ...