Incorporating Biological Knowledge with Factor Graph Neural Network for Interpretable Deep Learning

by   Tianle Ma, et al.

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.


page 1

page 2

page 3

page 4


Explainable Multilayer Graph Neural Network for Cancer Gene Prediction

The identification of cancer genes is a critical, yet challenging proble...

Biophysical models of cis-regulation as interpretable neural networks

The adoption of deep learning techniques in genomics has been hindered b...

Incorporating Prior Knowledge in Deep Learning Models via Pathway Activity Autoencoders

Motivation: Despite advances in the computational analysis of high-throu...

Graph-in-Graph Network for Automatic Gene Ontology Description Generation

Gene Ontology (GO) is the primary gene function knowledge base that enab...

Interpretable Drug Synergy Prediction with Graph Neural Networks for Human-AI Collaboration in Healthcare

We investigate molecular mechanisms of resistant or sensitive response o...

Biological Factor Regulatory Neural Network

Genes are fundamental for analyzing biological systems and many recent w...

Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks

A biological system is a complex network of heterogeneous molecular enti...

Please sign up or login with your details

Forgot password? Click here to reset