Environment:

• GPU: NVIDIA Tesla V100 SXM2
• Python: python3.9

Installation

conda create -n biorex python=3.9
conda activate biorex
pip install -r requirements.txt

Train and evaluate BioREx

Step 1: Download the datasets for BioREx

You can download our converted datasets, and unzip it to

datasets/

If you want to convert the datasets by yourself, you can use the below script to convert original datasets into our input format.

bash scripts/build_biorex_datasets.sh

Step 2: Download the pre-trained model

Please download the model here

Then put them into

microsoft/

Step 3: Train and evaluate

bash scripts/run_biorex_exp.sh <CUDA_VISIBLE_DEVICES>

Please replace the above <CUDA_VISIBLE_DEVICES> with your GPUs' IDs. Eg: '0,1' for GPU devices 0 and 1. For example

bash scripts/run_biorex_exp.sh 0,1

BioREx pre-trained models

You can download the BioREx pre-trained models:

• BioREx BioLinkBERT model (Preferred) is utilized in the beta version of PubTator3.
• BioREx PubMedBERT model (Original)

Predicting New Data:

If you only wish to use our tool for predicting new data without the need for training, please follow the steps outlined below:

Download the BioREx pre-trained model BioREx model file and place it in the "BioREx/" directory.

Open the "scripts/run_test_pred.sh" file and modify the values of the variables "in_pubtator_file" and "out_pubtator_file" to match your input PubTator file (with annotations) and the desired output PubTator file (where predicted relations will be stored).

Execute the following script to initiate the prediction process:

bash scripts/run_test_pred.sh <CUDA_VISIBLE_DEVICES>

Please replace the above <CUDA_VISIBLE_DEVICES> with your GPUs' IDs. Eg: '0,1' for GPU devices 0 and 1. For example

bash scripts/run_test_pred.sh 0,1

Citing BioREx

• Lai P. T., Wei C. H., Luo L., Chen Q. and Lu Z. BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets. Journal of Biomedical Informatics. 2023.

@article{lai2023biorex,
  author  = {Lai, Po-Ting and Wei, Chih-Hsuan and Luo, Ling and Chen, Qingyu and Lu, Zhiyong},
  title   = {BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets},
  journal = {Journal of Biomedical Informatics},
  volume  = {146},
  pages   = {104487},
  year    = {2023},
  issn    = {1532-0464},
  doi     = {https://doi.org/10.1016/j.jbi.2023.104487},
  url     = {https://www.sciencedirect.com/science/article/pii/S1532046423002083},
}

Acknowledgments

This research was supported by the NIH Intramural Research Program, National Library of Medicine. It was also supported by the National Library of Medicine of the National Institutes of Health under award number 1K99LM014024 (Q. Chen) and the Fundamental Research Funds for the Central Universities [DUT23RC(3)014 to L.L.].

Disclaimer

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