Implementation for paper: GCN-BMP: Investigating graph representation learning for DDI prediction task
Please refer to markdown files in every sub-directories to download datasets, best trained model and baseline performances. For performance comparison purposes, part of the code is modified from previous work.
This work is done in Multimedia Signal and Intelligent Information Processing (MSIIP) lab, Department Of Electronic Engineering, Tsinghua University.
If you find GCN-BMP useful in your research, we ask that you cite the following papers:
@inproceedings{Chen2019,
doi = {10.1109/bibm47256.2019.8983416},
url = {https://doi.org/10.1109/bibm47256.2019.8983416},
year = {2019},
month = nov,
publisher = {{IEEE}},
author = {Xin Chen and Xien Liu and Ji Wu},
title = {Drug-drug Interaction Prediction with Graph Representation Learning},
booktitle = {2019 {IEEE} International Conference on Bioinformatics and Biomedicine ({BIBM})}
}
@article{Chen2020,
doi = {10.1016/j.ymeth.2020.05.014},
url = {https://doi.org/10.1016/j.ymeth.2020.05.014},
year = {2020},
month = jul,
publisher = {Elsevier {BV}},
volume = {179},
pages = {47--54},
author = {Xin Chen and Xien Liu and Ji Wu},
title = {{GCN}-{BMP}: Investigating graph representation learning for {DDI} prediction task},
journal = {Methods}
}