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Found 3 papers, 1 papers with code
Incorporating Biological Knowledge with Factor Graph Neural Network for Interpretable Deep Learning
To address this challenge, we developed the Factor Graph Neural Network model that is interpretable and predictable by combining probabilistic graphical models with deep learning.
Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis
Our framework employs deep representation learning to learn feature embeddings and patient embeddings simultaneously, enabling us to integrate feature interaction network and patient view similarity network constraints into the training objective.
AffinityNet: semi-supervised few-shot learning for disease type prediction
The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not.
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