MiRGraph

The source code of the paper “MiRGraph: learning of microRNA-mRNA interactomes from heterogeneous gene regulatory network and genomic sequences”

Runing Environment

python 3.11.8

cuda 12.1

pytorch 2.2.1

torch_geometric 2.5.0

ryp2

Scenario 1

Prepreocessed Data

Input data can be obtained from this link [Input Data] (https://drive.google.com/file/d/1-oYgciNZEe-ubRLzwB9BSWLDlZR7Pz3J/view?usp=drive_link).

1. File 'dataCombine_negall.pkl' used as the input of model with network.
2. File 'dataSplit_negall.pkl' used as the input of model without network.

Training Model

1. After configuring the environment, directly run the .py file in the ./scenario 1/train/ folder:

• running file 'HGT_BiLSTM_gpu_mlp.py' to train HGT_BiLSTM.
• running file 'HGT_linkloader.py' to train HGT.
• running file 'RGCN_BiLSTM_gpu_directDot.py' to train MRMTI.
• running file 'RGCN_gpu_directDot.py' to train RGCN.
• running file 'TransCNN.py' to train TransCNN.

2. For file 'miRGraph_endtoend_cpu_pre_nodj_0.0001.py', we should:

• First, running file 'HGT_linkloader.py' and 'TransCNN.py' to pretrain HGT and TransCNN.
• Then, running file 'HGT&TransCNN_embedding.ipynb' to obtain the parameters of HGT and TransCNN in miRGraph_endtoend.
• Finally, running file 'miRGraph_endtoend_cpu_pre_nodj_0.0001.py' to train the model miRGraph_endtoend.

3. For file 'miRGraph_stepbystep_gpu_0.001.py',we should:

• First, running file 'HGT_linkloader.py' and 'TransCNN.py' to pretrain HGT and TransCNN.
• Then, running file 'HGT&TransCNN_embedding.ipynb' to obtain the embedding of gene and miRNA.
• Finally, running file 'miRGraph_stepbystep_gpu_0.001.py' to train the model miRGraph_stepbystep.

Testing Model

1. Directly running the .ipynb file in the ./scenario 1/test/ folder to obtain the testing results of all methods.
2. Testing results of all methods are in ./scenario 1/test/results/ folder, directly running file 'AllmethodMetric.ipynb' can obtain metrics of them.

Scenario 2

Prepreocessed Data

Input data can be obtained from this link [Input Data] (https://drive.google.com/file/d/1-oYgciNZEe-ubRLzwB9BSWLDlZR7Pz3J/view?usp=drive_link).

1. File 'dataCombine_negall_usingmiRNAanchor.pkl' used as the input of model with network.
2. File 'dataSplit_negall_usingmiRNAanchor.pkl' used as the input of model without network.

Training Model

1. After configuring the environment, directly run the .py file in the ./scenario 2/train/ folder:

• running file 'HGT_BiLSTM_cpu_usingmiRNAanchor.py' to train HGT_BiLSTM.
• running file 'HGTfull_usingmiRNAanchor.py' to train HGT.
• running file 'RGCN_BiLSTM_cpu_usingmiRNAanchor.py' to train MRMTI.
• running file 'RGCN_cpu_usingmiRNAanchor.py' to train RGCN.
• running file 'TransCNN_usingmiRNAanchor.py' to train TransCNN.

2. For file 'miRGraph_endtoend_cpu_pre_nodj_0.0001.py', we should:

• First, running file 'HGTfull_usingmiRNAanchor.py' and 'TransCNN_usingmiRNAanchor.py' to pretrain HGT and TransCNN.
• Then, running file 'HGT&TransCNN_embedding_usingmiRNAanchor.ipynb' to obtain the parameters of HGT and TransCNN in miRGraph_endtoend.
• Finally, running file 'miRGraph_endtoend_cpu_pre_nodj_0.0001_usingmiRNAanchor.py' to train the model miRGraph_endtoend.

3. For file 'miRGraph_stepbystep_gpu_0.001.py',we should:

• First, running file 'HGTfull_usingmiRNAanchor.py' and 'TransCNN_usingmiRNAanchor.py' to pretrain HGT and TransCNN.
• Then, running file 'HGT&TransCNN_embedding_usingmiRNAanchor.ipynb' to obtain the embedding of gene and miRNA.
• Finally, running file 'miRGraph_stepbystep_gpu_0.001_usingmiRNAanchor.py' to train the model miRGraph_stepbystep.

Testing Model

1. Directly running the .ipynb file in the ./scenario 2/test/ folder to obtain the testing results of all methods.
2. Testing results of all methods are in ./scenario 2/test/results/ folder, directly running file 'AllmethodMetric_usingmiRNA.ipynb' can obtain metrics of them.