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Found 3 papers, 3 papers with code
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks
A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic.
Explainable Artificial Intelligence (XAI)
General Classification
+3
Self-supervised edge features for improved Graph Neural Network training
In recent years, there has been a lot of work incorporating edge features along with node features for prediction tasks.
Disease State Prediction From Single-Cell Data Using Graph Attention Networks
To the best of our knowledge, this is the first effort to use graph attention, and deep learning in general, to predict disease state from single-cell data.
