Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs, and diseases (GCLMTP)

1. Overview

The code for paper "Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs, and diseases". The repository is organized as follows:

• data/ contains the datasets used in the paper;
• code/similarity_calculation.py is the calculation and integration of lncRNA/miRNA/disease similarities;
• code/model.pycontains unsupervised graph contrastive learning module to extract lncRNA/miRNA/disease node embeddings;
• code/LDA_prediction.py contains multiple classifiers to infer the lncRNA-disease association scores (e.g., AdaBoost, XGBoost...);
• code/MDA_prediction.py contains multiple classifiers to infer the miRNA-disease association scores (e.g., AdaBoost, XGBoost...);
• code/LMI_prediction.py contains multiple classifiers to infer the lncRNA-miRNA interaction scores (e.g., AdaBoost, XGBoost...);

2. Dependencies

• numpy == 1.24.2
• pandas == 1.4.4
• torch == 1.13.1
• sklearn == 1.2.2
• xgboost == 1.7.5
• lightgbm == 3.3.5

3. Quick Start

Here we provide a example to predict the association scores among lncRNAs, miRNAs, and diseases:

1. Download and upzip our data and code files
2. Run similarity_calculation.py to calculate lncRNA/miRNA/disease similarity and save them to ./dataset
3. Run model.py to generate low-dimensional embeddings of lncRNA/miRNA/disease nodes and save them to ./result
4. Run LDA_prediction.py or MDA_prediction.py or LMI_prediction.py to obtain the lncRNA-disease association scores, miRNA-disease association scores, and lncRNA-miRNA interaction scores, respectively. You can choose different classifiers, including Adaboost, XGBoost, GBDT, LightGBM, MLP, RF.

4. Contacts

If you have any questions, please email Nan Sheng (shengnan21@mails.jlu.edu.cn)

5. Workflow of GCLMTP