scGraph2Vec is a deep generative model for gene embedding augmented by variational graph autoencoder (VGAE) and single-cell omics data (scRNA-seq or scATAC-seq) ...

In this paper, we attempt to address this issue by introducing two significant changes to Variational Graph Autoencoder for Community Detection (VGAECD). Firstly, we introduce a simplified graph ...

To address these issues, we incorporate L2 regularization and decoupling techniques into the variational graph autoencoder framework, proposing a snoRNA-disease association prediction model, named ...

Methods: In this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA) to infer potential microbedrug associations by combining ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...

This repository contains our implementation of Constrained Graph Variational Autoencoders for Molecule Design (CGVAE). @article{liu2018constrained, title={Constrained Graph Variational Autoencoders ...