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 paper, we propose a novel recommendation framework named CVGAE (short for camouflaged variational graph autoencoder), which effectively models user behaviors and mitigates the risk of user ...

Rebuilding BIM: Motif. BIM 2.0: why it’s time to reinvent the tools that power the built world. Amar Hanspal, CEO, Motif ...

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 ...