{"id":13958618,"url":"https://github.com/biomed-AI/CoSMIG","last_synced_at":"2025-07-21T00:31:25.867Z","repository":{"id":45224054,"uuid":"410782268","full_name":"biomed-AI/CoSMIG","owner":"biomed-AI","description":"Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction 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药物-药物 化合物-蛋白质 相互作用"],"sub_categories":["网络服务_其他"],"readme":"# CoSMIG\nCommunicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction\n\n![alt text](https://github.com/Jh-SYSU/CoSMIG/blob/main/framework.jpg \"Illustration of CoSMIG\")\n\n\nThis is the standalone code for our paper: [Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction](https://arxiv.org/abs/2205.05957)\n\n## Requirements\n\nStable version: Python 3.7.9 + PyTorch 1.7.1+cu110 + PyTorch_Geometric 1.6.3.\n\n\nInstall [PyTorch](https://pytorch.org/)\n\nInstall [PyTorch_Geometric](https://rusty1s.github.io/pytorch_geometric/build/html/notes/installation.html)\n\nOther required python libraries: numpy, scipy, pandas, h5py, networkx, tqdm etc.\n\nAlso you can  install the required packages follow there instructions (tested on a linux terminal):\n\n`conda env create -f environment.yaml`\n\n\n## Datasets\n\nPlease Contact us (raojh6@mail2.sysu.edu.cn) to obtain the Data (from DrugBank and DGIdb) and Splits.\n\n### Statistic of DGI Dataset\n|Dataset|DrugBank|DGIdb|\n|:-:|:-:|:-:|\n|#Drug|425|1185|\n|#Gene|11284|1164|\n|#Interactions|80924|11266|\n|Interaction type|2|14|\n\n## Usages\nFor training on DrugBank on the transductive scenario:\n```\nCUDA_VISIBLE_DEVICES=0 python main.py --data-name DrugBank --testing --dynamic-train --dynamic-test --dynamic-val --save-results --max-nodes-per-hop 200\n```\n\n\nFor training on DGIdb on the inductive scenario:\n```\nCUDA_VISIBLE_DEVICES=0 python main.py --data-name DGIdb --testing --mode inductive --dynamic-train --dynamic-test --dynamic-val --save-results --max-nodes-per-hop 200\n```\n\nMore parameters could be found by:\n```\npython main.py -h\n```\n\n## Reference\nIf you find the code useful, please cite our paper.\n```\n@inproceedings{cosmig,\n  title     = {Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction},\n  author    = {Rao, Jiahua and Zheng, Shuangjia and Mai, Sijie and Yang, Yuedong},\n  booktitle = {Proceedings of the Thirty-First International Joint Conference on\n               Artificial Intelligence, {IJCAI-22}},\n  publisher = {International Joint Conferences on Artificial Intelligence Organization},\n  editor    = {Lud De Raedt},\n  pages     = {3919--3925},\n  year      = {2022},\n  month     = {7},\n  note      = {Main Track},\n  doi       = {10.24963/ijcai.2022/544},\n  url       = {https://doi.org/10.24963/ijcai.2022/544},\n}\n```\n\n## Contact\nJiahua Rao (raojh6@mail2.sysu.edu.cn) and Yuedong Yang (yangyd25@mail.sysu.edu.cn)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiomed-AI%2FCoSMIG","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbiomed-AI%2FCoSMIG","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiomed-AI%2FCoSMIG/lists"}