Published in arxiv, 2024
drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs.
Published in bioRxiv, 2024
In this study, we introduce a Bi-Graph Convolutional Network (BiGCN), a deep learning method that leverages both cell similarities and gene co-expression to capture cell-type-specific gene co-expression patterns for imputing scRNA-seq data.
Published in Arxiv, 2024
This study introduces scVGAE, an innovative approach integrating GCN into a variational autoencoder framework while utilizing a ZINB loss function.
Published in arxiv, 2024
A multi-agent system for interpretable drug target interaction
Published in Heliyon, 2023
This study introduces a novel machine learning methodology for predicting GlutoPeak test parameters from image data, leveraging AutoKeras and transfer learning.