Abstract
The tremendous success of graphical neural networks (GNNs) has already had a major impact on systems biology research. For example, GNNs are currently used for drug target recognition in protein-drug interaction networks as well as cancer gene discovery and more. Important aspects whose practical relevance is often underestimated are comprehensibility, interpretability, and explainability. In this work, we present a graph-based deep learning framework for disease subnetwork detection via explainable GNNs. In our framework, each patient is represented by the topology of a protein-protein network (PPI), and the nodes are enriched by molecular multimodal data, such as gene expression and DNA methylation. Therefore, our novel modification of the GNNexplainer for model-wide explanations can detect potential disease subnetworks, which is of high practical relevance. The proposed methods are implemented in the GNN-SubNet Python program, which we have made freely available on our GitHub for the international research community (https://github.com/pievos101/GNN-SubNet).
Competing Interest Statement
The authors have declared no competing interest.
Abbrevations
- CNN
- Convolutional Neural Network
- GNN
- Graph Neural Network
- GIN
- Graph Isomorphism Network
- MLP
- Multi Layer Perceptron
- AI
- Artificial Intelligence
- XAI
- Explainable Artificial Intelligence
- DT
- Decision Tree
- DF
- Decision Forest
- TCGA
- The Cancer Genome Atlas
- PPI
- Protein-Protein Interaction Network
- KIRC
- Kidney Renal Clear Cell Carcinoma
- BRCA
- Breast Invasive Carcinoma
- LUAD
- Lung Adenocarcinoma
- ReLU
- Rectifiec Linear Unit