A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...

In this paper, we propose the adversarial attention variational graph autoencoder (AAVGA), which is a novel framework that incorporates attention networks into the encoder part and uses an adversarial ...

In this work, we propose a physics-guided variational graph autoencoder whose graph convolutional operator is derived from the PDE defining the convection-diffusion physical process. We compare ...

In this study, we propose MOLGAECL, a novel approach based on graph autoencoder pretraining and molecular graph contrastive learning. Initially, a large number of unlabeled molecular graphs are ...

Background: Artificial patient technology could transform health care by accelerating diagnosis, treatment, and mapping clinical pathways. Deep learning methods for generating artificial data in ...

Description of the block copolymer SAXS–SEM morphology characterization dataset, image data preprocessing procedures, python packages utilized and the usages of each package, the variational ...

2.2 Link prediction algorithm Graph autoencoder (GAE) and variational graph autoencoder (VGAE) models have been demonstrated as efficient tools to learn graph embeddings in an unsupervised way and ...

We call our approach Variational Autoencoder Modular Bayesian Network (VAMBN). Due to its generative nature, VAMBN allows for simulating virtual subjects by first drawing a sample from the BN and ...