Copyright (C) 2022 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.
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GATCL2CD is effective to predict associations between circRNAs and diseases, which is based on feature convolution learning with heterogeneous graph attention network.
- torch == 1.7.1+cu110
- numpy == 1.19.5
- matplotlib == 3.5.1
- dgl-cu110 == 0.5.3
- GAT_layer_v2: Coding multi-head dynamic attention mechanism.
- GATCL.py: the core model proposed in the paper.
- fivefold_CV.py: completion of a 5-fold cross-validation experiment.
- case_study.py: get scores for candidate circRNAs for all diseases.
- There are five state-of-the-art models including: DMFCDA, CD_LNLP, RWR, GATCDA, IGNSCDA, which are compared under the same experiment settings.
- 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