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Author: ychuest

README.md

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+# License
+
+Copyright (C) 2023 Li Peng ([email protected]), Cheng Yang ([email protected])
+
+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
+MRLHGNN is effective tool for drug repositioning and we are thankful that [Gu et al.](https://www.sciencedirect.com/science/article/pii/S0010482522008356) have published part of their data which can be used directly.
+
+
+
+# Environment Requirement
++ 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
+
+
+
+# Model
++ load_data.py: Constructing heterogeneous graph.
++ SeHG.py: the core model proposed in the paper.
+
+
+
+# Compare_models
+ 
+* NTSIM (2017)
+    * Proposed in [Predicting drug-disease associations based on the known association bipartite network](https://ieeexplore.ieee.org/abstract/document/8217698/), BIBM 2017.
+
+* BNNR (2019)
+    * Proposed in [Drug repositioning based on bounded nuclear norm regularization](https://doi.org/10.1093/bioinformatics/btz331), Bioinformatics 2019.
+
+* HGIMC (2020)
+    * Proposed in [Heterogeneous graph inference with matrix completion for computational drug repositioning](https://doi.org/10.1093/bioinformatics/btaa1024), Bioinformatics 2020.
+
+* NIMCGCN (2020)
+    * Proposed in [Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction](https://doi.org/10.1093/bioinformatics/btz965), Bioinformatics 2020.
+
+* LAGCN (2021)
+    * Proposed in [Predicting drug–disease associations through layer attention graph convolutional network](https://doi.org/10.1093/bib/bbaa243), Briefings in Bioinformatics 2021.
+
+* DRHGCN (2021)
+    * Proposed in [Drug repositioning based on the heterogeneous information fusion graph convolutional network](https://doi.org/10.1093/bib/bbab319), Briefings in Bioinformatics 2021.
+
+* DRWBNCF (2022)
+    * Proposed in [A weighted bilinear neural collaborative filtering approach for drug repositioning](https://doi.org/10.1093/bib/bbab581), Briefings in Bioinformatics 2022.
+
+* REDDA (2022)
+    * Proposed in [REDDA: Integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction](https://www.sciencedirect.com/science/article/pii/S0010482522008356), Computers in Biology and Medicine 2022.
+
+* MilGNet (2022)
+    * Proposed in [MilGNet: a multi-instance learning-based heterogeneous graph network for drug repositioning](https://ieeexplore.ieee.org/abstract/document/9995152/), BIBM 2022.
+
+
+
+# Question
++ If you have any problems or find mistakes in this code, please contact with us: 
+Cheng Yang: [email protected] ; Li Peng: [email protected]