Copyright (C) 2019 Jin Li(lijin@ynu.edu.cn)
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, see http://www.gnu.org/licenses/.
Jin Li(lijin@ynu.edu.cn) School of Software, Yunnan University, Kunming CHINA, 650000
NIMCGCN NIMCGCN: Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction (Bioinformatics).
Requirements Pytorch (tested on version 1.1.1) numpy (tested on version 1.16.2) sklearn (tested on version 0.20.3)
Quick start To reproduce our results: Unzip data.zip in ./data. Run main.py to RUN NIMCGCN.
Data description d-d.csv:disease-disease similarity matrix. m-m.csv: miRNA-miRNA similarity matrix. disease name.csv: list of disease names. miRNA name.csv: list of miRNA names m-d.csv: miRNA-disease association matrix
Citation information : Jin Li, Sai Zhang, Tao Liu, Chenxi Ning, Zhuoxuan Zhang and Wei Zhou. Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction. Bioinformatics, Volume 36, Issue 8, 15 April 2020, Pages 2538–2546. doi: 10.1093/bioinformatics/btz965.