Code for our paper "Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training" (IEEE BIBM 2021)
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/LPIGAC.git
cd LPIGAC
python fivefoldcv.pyWe 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=144, help='Dimension of representations')
parser.add_argument('--alpha', type=float, default=0.5,
help='Weight between lncRNA space and protein space')
parser.add_argument('--beta', type=float, default=1.0,
help='Hyperparameter beta')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()Files of data are listed as follow:
LncRNAName.txtincludes the names of all lncRNAs.ProteinName.txtincludes the names of all proteins.interaction.txtis a matrixYthat shows lncRNA-protein associations.Y[i,j]=1if lncRNAiand proteinjare known to be associated, otherwise 0.protfeat.txtis the feature matrix of proteins.rnafeat.txtis the feature matrix of lncRNAs.
@inproceedings{jin2021lpigac,
author = {Jin, Chen and Shi, Zhuangwei and Zhang, Han and Yin, Yanbin},
title = {Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training},
year = {2021},
booktitle = {IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
}