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"

Requirements

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

Usage

git clone https://github.com/zhanglabNKU/NIMGSA.git
cd NIMGSA
python fivefoldcv.py

Options

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()

Data

Files of data are listed as follow:

d-d.txt is a matrix Sd that includes the similarity of all diseases, Sd[i,j] denotes the similarity between disease i and disease j .
disease name.csv is a table that lists the name of all diseases.
m-d.txt is a matrix Y that shows miRNA-disease associations. Y[i,j]=1 if miRNA i and disease j are known to be associated, otherwise 0.
m-m.txt is a matrix Sm that includes the similarity of all miRNAs, Sm[i,j] denotes the similarity between miRNA i and miRNA j ..
miRNA name.csv is a table that lists the name of all miRNAs.

Citation

@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},
}

zhanglabNKU / NIMGSA