# GCLMTP

**Repository Path**: Tomhappy/GCLMTP

## Basic Information

- **Project Name**: GCLMTP
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-22
- **Last Updated**: 2023-10-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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.py`contains 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
![模型图](https://user-images.githubusercontent.com/95516781/236208254-813b1165-7679-4252-8534-daf60cb1b940.jpg)