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-# 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.
-
-![MOGONET](https://github.com/txWang/MOGONET/blob/master/MOGONET.png?raw=true "MOGONET")
-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>
-
-## Files
-*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
+# 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.
+
+![MOGONET](https://github.com/txWang/MOGONET/blob/master/MOGONET.png?raw=true)
+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>
+
+## Files
+*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