  While deep learning has achieved great success in many fields, one common
criticism about deep learning is its lack of interpretability. In most cases,
the hidden units in a deep neural network do not have a clear semantic meaning
or correspond to any physical entities. However, model interpretability and
explainability are crucial in many biomedical applications. 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. We directly encode biological knowledge such as Gene Ontology as
a factor graph into the model architecture, making the model transparent and
interpretable. Furthermore, we devised an attention mechanism that can capture
multi-scale hierarchical interactions among biological entities such as genes
and Gene Ontology terms. With parameter sharing mechanism, the unrolled Factor
Graph Neural Network model can be trained with stochastic depth and generalize
well. We applied our model to two cancer genomic datasets to predict target
clinical variables and achieved better results than other traditional machine
learning and deep learning models. Our model can also be used for gene set
enrichment analysis and selecting Gene Ontology terms that are important to
target clinical variables.
