KG-MTL
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Source code for TKDE'22 "KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular Interaction"
KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular Interaction
This is our implementation of the paper
Tengfei Ma, Xuan Lin, Bosheng Song, Philip S. Yu and Xiangxiang Zeng. KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular Interaction. TKDE' 2022 accepted
Requirement
- pytorch==1.5.0
- dglteam==dgl-cuda10.1
- rdkit==2018.09.3
- dgllife
Installation
You can create a virtual environment using conda.
conda create -n kg-mtl python=3.7
source activate kg-mtl
git clone https://github.com/xzenglab/KG-MTL.git
cd KG-MTL
conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.1 -c pytorch
conda install -c dglteam dgl-cuda10.1
conda install -c rdkit rdkit==2018.09.3
pip install dgllife
Datasets
We provide the files of knowledge graph DRKG, and the datasets used in DTI and CPI tasks.
- DRKG https://github.com/gnn4dr/DRKG
- BindingDB https://www.bindingdb.org/bind/index.jsp
- Human http://admis.fudan.edu.cn/negative-cpi/
- C.elegans http://admis.fudan.edu.cn/negative-cpi/
- DrugBank https://go.drugbank.com/releases/latest
- DrugCentral https://drugcentral.org/
However, we found that the original links of the Human and C.elegans datasets were unavailable, so we put the source files in the path datasets/original_cpi. In the repository https://github.com/xiaomingaaa/drugbank, we provide code and processed files that can be used for entity id conversion among databases.
Usage
usage: main.py [-h] [--dropout DROPOUT] [--n-hidden N_HIDDEN] [--gpu GPU]
[--lr_pre LR_PRE] [--lr_dti LR_DTI] [--n_bases N_BASES]
[--sample_size SAMPLE_SIZE] [--n-layers N_LAYERS]
[--n-epochs N_EPOCHS] [--regularization REGULARIZATION]
[--grad-norm GRAD_NORM] [--graph-split-size GRAPH_SPLIT_SIZE]
[--negative-sample NEGATIVE_SAMPLE]
[--edge-sampler EDGE_SAMPLER]
[--graph_batch_size GRAPH_BATCH_SIZE]
[--rgcn_epochs RGCN_EPOCHS] [--loss_lamda LOSS_LAMDA]
[--cpi_dataset CPI_DATASET] [--dti_dataset DTI_DATASET]
[--shared_unit_num SHARED_UNIT_NUM] [--embedd_dim EMBEDD_DIM]
[--variant VARIANT] [--loss_mode LOSS_MODE]
--loss_mode weighted is the learning stratagies, and 'weighted' represents the certainty is used.
python main.py --loss_mode weighted --variant KG-MTL-C --gpu 1 --cpi_dataset human --dti_dataset drugcentral
You can change the parameter '--variant' to run different variants (i.e., KG-MTL-C, KG-MTL-L, and KG-MTL-S) of KG-MTL. And you can adjust the multi-task learning strategy by modifying the parameter '--loss_mode'.
Citation
@ARTICLE{kg_mtl_22,
author={Ma, Tengfei and Lin, Xuan and Song, Bosheng and Yu, Philip S. and Zeng, Xiangxiang},
journal={IEEE Transactions on Knowledge and Data Engineering},
title={KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular Interaction},
year={2022},
volume={},
number={},
pages={1-12},
doi={10.1109/TKDE.2022.3188154}}
For any clarification, comments, or suggestions please create an issue or contact Tengfei Ma.
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