Methods: We developed a Deep Hierarchical Conditional Variational Autoencoder (CVAE) for de novo ACP design, using transfer learning by initializing the ESM-2 pre-trained encoder. A comprehensive ACP ...

Autoencoders have been successfully used for graph embedding, and many variants have been proven to effectively express graph data and conduct graph analysis in low-dimensional space. However, ...

Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning, Stockholm, 10-15 July 2018, 2323-2332.

PerturbNet is a generative AI model that can predict shifts in cell state—changes in overall gene expression—in response to ...

The junction tree variational autoencoder (JTVAE), pioneered by Jin et al., (22) represents a significant breakthrough in molecular VAE models. This model innovatively segments the molecular graph ...

Introduction: Dementia, characterized by cognitive decline and impaired judgment, imposes a significant economic burden due to its rising prevalence and high diagnostic costs. Recent research has ...

They transform the challenge of preserving structure information into maintaining inter-node similarity between the non-Euclidean, high-dimensional latent space and the Euclidean input space. For ...

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 ...

A technical paper titled “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques” was published by researchers at Commonwealth Scientific and ...