Copyright (C) 2023 Li Peng (plpeng@hnu.edu.cn), Cheng Yang (yangchengyjs@163.com)

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/.

MRLHGNN is effective tool for drug repositioning and we are thankful that Gu et al. have published part of their data which can be used directly.

• torch version (GPU) == 2.0.1
• CUDA version == 12.0
• numpy == 1.34.3
• matplotlib == 3.5.1
• dgl-cu118 == 1.1.0
• pandas == 1.5.3
• scikit-learn == 1.2.2
• torch-cluster == 1.6.1+pt20cu118
• torch-scatter == 2.1.1+pt20cu118
• torch-sparse == 0.6.17+pt20cu118
• torch-spline-conv == 1.2.2+pt20cu118
• torchaudio ==2.0.2
• torchvision == 0.15.2

• load_data.py: Constructing heterogeneous graph.
• SeHG.py: the core model proposed in the paper.

• NTSIM (2017)

  • Proposed in Predicting drug-disease associations based on the known association bipartite network, BIBM 2017.

• BNNR (2019)

  • Proposed in Drug repositioning based on bounded nuclear norm regularization, Bioinformatics 2019.

• HGIMC (2020)

  • Proposed in Heterogeneous graph inference with matrix completion for computational drug repositioning, Bioinformatics 2020.

• NIMCGCN (2020)

  • Proposed in Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction, Bioinformatics 2020.

• LAGCN (2021)

  • Proposed in Predicting drug–disease associations through layer attention graph convolutional network, Briefings in Bioinformatics 2021.

• DRHGCN (2021)

  • Proposed in Drug repositioning based on the heterogeneous information fusion graph convolutional network, Briefings in Bioinformatics 2021.

• DRWBNCF (2022)

  • Proposed in A weighted bilinear neural collaborative filtering approach for drug repositioning, Briefings in Bioinformatics 2022.

• REDDA (2022)

  • Proposed in REDDA: Integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction, Computers in Biology and Medicine 2022.

• MilGNet (2022)

  • Proposed in MilGNet: a multi-instance learning-based heterogeneous graph network for drug repositioning, BIBM 2022.

• If you have any problems or find mistakes in this code, please contact with us: Cheng Yang: yangchengyjs@163.com ; Li Peng: plpeng@hnu.edu.cn