Predicting miRNA–disease association based on neural inductive matrix completion with graph autoencoders and self-attention mechanism
Code for our paper "Predicting miRNA–disease association based on neural inductive matrix completion with graph autoencoders and self-attention mechanism"
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/NIMGSA.git
cd NIMGSA
python fivefoldcv.py
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=300,
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-7,
help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=64,
help='Dimension of representations')
parser.add_argument('--alpha', type=float, default=0.5,
help='Weight between miRNA 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 of data are listed as follow:
d-d.txt
is a matrixSd
that includes the similarity of all diseases,Sd[i,j]
denotes the similarity between diseasei
and diseasej
.disease name.csv
is a table that lists the name of all diseases.m-d.txt
is a matrixY
that shows miRNA-disease associations.Y[i,j]=1
if miRNAi
and diseasej
are known to be associated, otherwise 0.m-m.txt
is a matrixSm
that includes the similarity of all miRNAs,Sm[i,j]
denotes the similarity between miRNAi
and miRNAj
..miRNA name.csv
is a table that lists the name of all miRNAs.
@article{jin2022nimgsa,
author = {Jin, Chen and Shi, Zhuangwei and Lin, Ken and Zhang, Han},
title = {Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism},
journal = {Biomolecules},
volume = {12},
year = {2022},
number = {1},
pages = {64},
}