Overview

GPUDMDA : Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning and deep neural network

Data

Data is available at HMDAD and Disbiome. In this work，HMDAD is data1 and Disbiome is data2.

Important document

• feature.py：This is used to learn the features of microbes and diseases from the similarity networks .
• k-means.py ：This is used to cluster positive samples, calculate the distance between each positive sample and the center of the class, and determine the number of spy samples.
• xgboot.py：This is used to screen for reliable negative samples.
• CV_123.py：This is used for MDAs classification.

Environment

Install python3.7 for running this model. And these packages should be satisfied:

• tensorflow-gpu $\approx$ 2.4.0
• pytorch $\approx$ 1.12.1+cu116
• xgboost $\approx$ 1.6.2
• numpy $\approx$ 1.19.5
• pandas $\approx$ 1.3.5
• scikit-learn $\approx$ 1.0.2
• matplotlib $\approx$ 3.5.2

Usage

Taking HMDAD as an example，default is 5-fold cross validation on microbe-Disease pairs，to run the model：

python data1/CV_123.py

The variable "cv" in the “CV. getcv()” function:

• “1” represents 5-fold cross validation on diseases.
• “2” represents 5-fold cross validation on microbes.
• “3” represents 5-fold cross validation on microbe-disease pairs.