You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hello, thank you for sharing this excellent tool. I have been following the README to reproduce the results with the pretrained model and evaluate specific diseases.
However, I noticed some discrepancies between the generated Ranked List column in the output and the Explorer.
Additionally, I am interested in extracting drug candidates for a specific disease by tracing the Meta-Paths that go through a particular biological entity (e.g., a protein). Could you please guide me on how to filter such Meta-Paths using the current implementation?
I would appreciate any information on this issue.
I am running the model with CUDA 11.0, dgl 0.5.3 and latest pytorch.
Problem solved.
FYI: set split to 'full_graph' for specific disease prediction.
I encountered an out of memory error while trying to run GraphMask.
Update: GraphMask model training requires large GPU usage. Check your device. It would be helpful to provide a pre-trained
GraphMask model. Additionally, I have some difficulties understanding the gates output. gates = TxGNN.retrieve_save_gates('SAVED_PATH')
I am unsure how to determine whether to retain an edge based on the two attention values. I couldn’t find related code on how the Meta-Path is being traced after gates output too.
The text was updated successfully, but these errors were encountered:
Hello, thank you for sharing this excellent tool. I have been following the README to reproduce the results with the pretrained model and evaluate specific diseases.
However, I noticed some discrepancies between the generated Ranked List column in the output and the Explorer.
Additionally, I am interested in extracting drug candidates for a specific disease by tracing the Meta-Paths that go through a particular biological entity (e.g., a protein). Could you please guide me on how to filter such Meta-Paths using the current implementation?
I would appreciate any information on this issue.
I am running the model with CUDA 11.0, dgl 0.5.3 and latest pytorch.
Problem solved.
FYI: set split to 'full_graph' for specific disease prediction.
I encountered an out of memory error while trying to run GraphMask.
Update: GraphMask model training requires large GPU usage. Check your device. It would be helpful to provide a pre-trained
GraphMask model. Additionally, I have some difficulties understanding the gates output.
gates = TxGNN.retrieve_save_gates('SAVED_PATH')
I am unsure how to determine whether to retain an edge based on the two attention values. I couldn’t find related code on how the Meta-Path is being traced after gates output too.
The text was updated successfully, but these errors were encountered: