MOGONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification
Tongxin Wang*, Wei Shao*, Zhi Huang, Haixu Tang, Jie Zhang, Zhengming Ding, and Kun Huang
MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.
Overview of MOGONET.
Illustration of MOGONET. MOGONET combines GCN for multi-omics specific learning and VCDN for multi-omics integration. MOGONET combines GCN for multi-omics specific learning and VCDN for multi-omics integration. For clear and concise illustration, an example of one sample is chosen to demonstrate the VCDN component for multi-omics integration. Pre-processing is first performed on each omics data type to remove noise and redundant features. Each omics-specific GCN is trained to perform class prediction using omics features and the corresponding sample similarity network generated from the omics data. The cross-omics discovery tensor is calculated from the initial predictions of omics-specific GCNs and forwarded to VCDN for final prediction. MOGONET is an end-to-end model and all networks are trained jointly.
main_mogonet.py: Examples of MOGONET for classification tasks
main_biomarker.py: Examples for identifying biomarkers
models.py: MOGONET model
train_test.py: Training and testing functions
feat_importance.py: Feature importance functions
utils.py: Supporting functions
* Equal contribution