In this work, by integrating SEAL, GraphSAGE and top-k pooling, we construct a GNN based model to predict ncRNA-protein interactions.

Python 3.6

pytorch 1.4.0

torch-geometric 1.4.2

dependencies for node2vec

Note: This project contains node2vec released in https://github.com/aditya-grover/node2vec. So you need to configure its dependencies according to its description. But this node2vec is based on Python2, and some of its dependencies are also based on Python2. The code for node2vec included in the project was modified to make it work under Python3.

Note: We strongly recommend that you configure a Python 2 environment to run node2vec and download the source code from this site.This will avoid a lot of confusion with Python2 and Python3.

Python .\src\generate_edgelist.py --projectName yourProjectName --interactionDatasetName NPInter2

This will output five edgelist file in 'data/graph/{yourProjectName}/training_{0-4}/bipartite_graph.edgelist' and five empty folder 'data/node2vec_result/{yourProjectName}/training_{0-4}' to store node2vec result.

• --projectName : The name of dataset you want to generate.
• --interactionName : The name of existing ncRNA-protein interaction dataset, like NPInter2, NPInter2_lncRNA, RPI7317,RPI2241, and RPI369.

• --createBalanceDataset : Default = 1. Creating balance dataset for unbalanced interaction dataset.
• --reduce: Default = 0. Whether you want to randomly reducing the interaction database, and also maintain one connected component. 1 means yes, 0 means no.
• --reduceRatio: Default = 0.5. The reduce Ratio of reduced dataset.

Python .\node2vec-master\src\main.py --input 'data/graph/yourProjectName/training_{0-4}/bipartite_graph.edgelist' --output 'data/node2vec_result/yourProjectName/training{0-4}/result.emb'

• --input : The input edgelist file generated by '.\src\generate_edgelist.py'.
• --output : The output node2vec result. This parameter has a pattern: 'data/node2vec_result/{yourProjectName}/result.emb'. The name of the output file must be result.emb, and the folder it in is the folder generated by '.\src\generate_edgelist.py'

• please see https://github.com/aditya-grover/node2vec

Python .\src\generate_dataset.py --projectName yourProjectName --interactionDatasetName NPInter2 --fold 0

Note: There is randomness in the existence process because the data is shuffled as the dataset is generated.

• --projectName : The name of dataset you want to generate.
• --interactionName : The name of existing ncRNA-protein interaction dataset, like NPInter2, NPInter2_lncRNA, RPI7317,RPI2241, and RPI369.
• --fold : Generating dataset for which fold, 0-4

• --createBalanceDataset : Default = 1. This parameter must keep it as same as you run src/generate_edgelist.py
• --shuffle: Default = 1. Shuffle interactions before generate dataset, 1 means yes, 0 means no.
• --noKmer: Default = 0. If you don't want to add k-mer frequencies into node feature, set it to 1;

Python .\src\train_with_twoDataset.py --trainingName nameOfTraining --datasetName yourProjectName --interactionDatasetName NPInter2 --epochNumber 50 --fold 0

This will save modules and training log in 'result/{yourProjectName}'.Every five epochs report the model performance and save the model. Please note that the paper uses the performance of the model with the largest mean MCC in a cross-validation.

Note: There is randomness in the existence process because the data is shuffled as the dataset is read.

Note: The initial learning rate was 0.0001 when we perform 5-fold cross validation on RPI369. Moreover, in the data set of RPI369, the model often fails to converge, which further proves that NPI-GNN is not suitable to run on the data set with too small local subgraphs.

• --trainingName: The name of this training.
• --datasetName: The name of dataset you want to use, and this is the same as {yourProjectName} in previous steps.
• --interactionName : The name of existing ncRNA-protein interaction dataset, like NPInter2, NPInter2_lncRNA, RPI7317,RPI2241, and RPI369.
• --fold : Which fold you want to run, 0-4.

• --epochNumber: Default = 50. The number of epoch of each fold.
• --hopNumber：Default = 2. The hop number of h-hop local enclosing subgraph.
• --initialLearningRate: Default = 0.001. The initial learning rate of this training.
• --l2WeightDecay: Default = 0.001. The L2 weight decay of this training.
• --batchSize: Default = 200. The batch size of this training.

• Make you interaction dataset an xlsx file : {yourInteractionDataset}.xlsx. The format of this xlsx file, please refer to existing xlsx in 'data/source_database_data'.

• Prepare lncRNA 3-mer frequencies result : lncRNA_3_mer.txt, and put it under 'data/lncRNA_3_mer/{yourInteractionDataset}'.

• Prepare protein fasta file, create new folder '{yourInteractionDataset}' under 'data/protein_2_mer/', use 'tool/protein_2-mer_generation.py' to generate protein 2-mer result

Python ./tool/protein_2-mer_generation.py --input_fasta_file 'Path_of_you_protein_fasta_file' --output_folder 'data/protein_2_mer/yourInteractionDataset' --k 2

• --input_fasta_file: The path of your protein fasta file.
• --output_folder : The folder you created : 'data/protein_2_mer/{yourInteractionDataset}'

• --k: Default = 2. The k of k-mer frequency.

When running the workflow using you on interaction dataset, all --interactionDataset have to be {yourInteractionDataset}.

python ./src/case_study_negativeSample.py --caseStudyName nameOfThisCaseStudy --projectName yourProjectName --fold 0 --interactionDatasetName NPInter2 --createBalanceDataset 1 --modelPath PathOfTheModel --threshold 0.95

This will create a folder in 'data/case_study/{nameOfThisCaseStudy}.

• --caseStudyName : The name of this case study.
• --projectName : The project Name in workflow 1-4
• --fold : 0-4, select one fold of the 5-fold cross-validation and complete the case study based on it.
• --interactionDatasetName : The name of existing ncRNA-protein interaction dataset, like NPInter2, NPInter2_lncRNA, RPI7317,RPI2241, and RPI369. Please keep it the same as you run workflow 1-4.
• --createBalanceDataset : 0 or 1, be consistent with workflow 1-4
• --modelPath : The path of trained NPI-GNN model
• --threshold : The threshold that determines the sample to be positive

• --noKmer : 0 or1, be consistent with workflow 1-4.

python ./src/generate_edgelist_NPInter2_RPI2241_mutual_interaction_study.py --projectName yourProjectName_1 --path_set_negativeInteractionKey yourNegativeInteractionKeyPath

This will generate a edgelist under 'data/graph/{yourProjectName_1}/bipartite_graph.edgelist' and a folder 'data/node2vec_result/{yourProjectName_1}'

• --projectName : the name of this project

• --path_set_negativeInteractionKey: default = 'data/set_allInteractionKey/1223_1/set_negativeInteractionKey_all', if you want to use other randomly selected negative sample, just change '1223_1' to other project name. But the project you choose must be based on NPInter2.

python ./node2vec-master/src/main.py --input data/graph/{yourProjectName_1}/bipartite_graph.edgelist -- output data/node2vec_result/{yourProjectName_1}/result.emb

./run_mutualInteraction_NPInter2.bat yourProjectName_1 1223_1

When generating edgelist, if you don't use the default negative sample, 1223_1 should be changed to the corresponding project name

python ./src/generate_edgelist_NPInter2_RPI2241_mutual_interaction_study.py --projectName yourProjectName_2 -- interactionDatasetName RPI2241 --createBalanceDataset 0

This will generate a edgelist under 'data/graph/{yourProjectName_2}/bipartite_graph.edgelist' and a folder 'data/node2vec_result/{yourProjectName_2}'

• --projectName : the name of this project, the project name here should be different from yourProjectName_1 in Step 1 and 2
• --interactionDatasetName : must be RPI2241
• --createBalanceDataset must be 0

python ./node2vec-master/src/main.py --input data/graph/{yourProjectName_2}/bipartite_graph.edgelist -- output data/node2vec_result/{yourProjectName_2}/result.emb

.\run_mutualInteraction_RPI2241.bat yourProjectName_2

python ./src/generate_edgelist.py --projectName nameOfThisReduction --reduce 1 --reduceRatio 0.25

This will generate a reduced NPInter2 at 'data/source_database_data/NPInter2_{reduceRatio}.xlsx'

• --projectName : The name of this reduction
• --reduce : must be 1
• --reduceRatio : The reduce ratio you want to use

python ./src/case_study.py --caseStudyName nameOfThisCaseStudy --projectName yourProjectName --fold 0 --interactionDatasetName NPInter2 --createBalanceDataset 1 --modelPath PathOfTheModel

This will create a folder in 'data/case_study/{nameOfThisCaseStudy}

• --caseStudyName : The name of this case study.
• --projectName : The project Name in workflow 1-4
• --fold : 0-4, select one fold of the 5-fold cross-validation and complete the case study based on it.
• --interactionDatasetName : The name of existing ncRNA-protein interaction dataset, like NPInter2, NPInter2_lncRNA, RPI7317,RPI2241, and RPI369. Please keep it the same as you run workflow 1-4.
• --createBalanceDataset : 0 or 1, be consistent with workflow 1-4
• --modelPath : The path of trained NPI-GNN model

• --noKmer : 0 or1, be consistent with workflow 1-4