VGAELDA: a representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Code for our paper "A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations"
The code has been tested running under Python 3.7.4, with the following packages and their dependencies installed:
numpy==1.16.5
pytorch==1.3.1
sklearn==0.21.3
git clone https://github.com/zhanglabNKU/VGAELDA.git
cd VGAELDA
python fivefoldcv.py --data 1
We adopt an argument parser by package argparse
in Python, and the options for running code are defined as follow:
parser = argparse.ArgumentParser()
parser.add_argument('--no-cuda', action='store_true', default=False,
help='Disables CUDA training.')
parser.add_argument('--seed', type=int, default=1, help='Random seed.')
parser.add_argument('--epochs', type=int, default=500,
help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01,
help='Learning rate.')
parser.add_argument('--weight_decay', type=float, default=1e-5,
help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=256,
help='Dimension of representations')
parser.add_argument('--alpha', type=float, default=0.5,
help='Weight between lncRNA space and disease space')
parser.add_argument('--data', type=int, default=1, choices=[1,2],
help='Dataset')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
Files in Dataset1 are listed as follow:
lncRNA_115.txt
includes the names of all 115 lncRNAs in Dataset1.disease_178.txt
includes the names of all 178 diseases in Dataset1.known_lncRNA_disease_interaction.txt
is a 115x178 matrixY
that shows lncRNA-disease associations.Y[i,j]=1
if lncRNAi
and diseasej
are known to be associated, otherwise 0.known_gene_disease_interaction.txt
is the feature matrix of diseases.rnafeat.txt
is the feature matrix of lncRNAs.
Files in Dataset2 are defined similarly to Dataset1.
@article{shi2021vgaelda,
author={Zhuangwei Shi and Han Zhang and Chen Jin and Xiongwen Quan and Yanbin Yin},
title={A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations},
journal={BMC Bioinformatics},
year={2021},
volume={22},
number={136},
pages={1-20},
url={https://doi.org/10.1186/s12859-021-04073-z},
}