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 MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.
 
-![MOGONET](https://github.com/txWang/MOGONET/blob/master/MOGONET.png?raw=true)
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 Overview of MOGONET. \
 <sup>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.<sup>