lynxkite / examples /ODE-GNN.lynxkite.json
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Add comments and groups to ODE-GNN workspace.
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"position": "top",
"type": {
"type": "None"
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"status": "done",
"title": "Mean pool"
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"text": "# Past tumor volume embedding with LSTM"
},
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"title": "Comment"
},
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"height": 130.0,
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"parentId": "Group 1",
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"params": {
"text": "# Gene-gene graph embedding"
},
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"title": "Comment"
},
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"id": "Comment 2",
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"params": {
"text": "# Learned initial drug & disease embeddings"
},
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"title": "Comment"
},
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"id": "Comment 3",
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"meta": {},
"params": {
"text": "# Extracting the graph embedding\n\nWe mean-pool the node embeddings of each node type, then concatenate these vectors."
},
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"title": "Comment"
},
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"height": 178.0,
"id": "Comment 4",
"parentId": "Group 3",
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"params": {
"text": "# Knowledge graph embedding with GNN\n\nThis heterogeneous graph convolution creates node embeddings for all genes, diseases, and drugs in the graph."
},
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"title": "Comment"
},
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"extent": "parent",
"height": 206.0,
"id": "Comment 5",
"parentId": "Group 4",
"position": {
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"params": {
"text": "# Tumor volume trajectory predictions with ODE\n\nAn MLP is used to predict the derivative of the tumor volume at different stages, based on the knowledge graph embedding and the past tumor volume embeddings."
},
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"title": "Comment"
},
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"id": "Comment 6",
"position": {
"x": -0.7721924881518737,
"y": -865.6155529412208
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"type": "comment",
"width": 400.0
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{
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"params": {
"text": "# Training configuration\n\nThe loss for the whole model is simply the MSE loss over the future volume measurements."
},
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"title": "Comment"
},
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"height": 138.0,
"id": "Comment 7",
"parentId": "Group 5",
"position": {
"x": 276.18914557042024,
"y": 267.8721350317928
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"text": "## Disease-gene and drug-gene edges"
},
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},
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"parentId": "Group 4",
"position": {
"x": 1615.9956563920114,
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