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This repository contains the codebase for our research project titled "Graph Attention Networks for Biomedical Insights: MultiOmics Integration for Risk Stratification and Biomarker ...

Here, we developed an MDA prediction method called GPUDMDA by combining feature extraction based on graph attention autoencoder (GATE), reliable negative MDA selection based on positive-unlabeled (PU) ...

Recently, top-k recommendation system is getting more and more attention from researchers and unlike the rating prediction task, the purpose of top-k recommendation is to present the user with a list ...

The structure of the feature autoencoder is shown in Figure 2A; it takes the gene expression matrix, composed of the first 2000 genes obtained after removing the low-expression cells and genes, and ...

The autoencoder consists of graph attention encoder and collaborative neural decoder, which is used to generate user and item latent vector accurately. And then we use the pairwise ranking learning ...