ANGEL_mm is a tool specifically designed for biomedical entity linking, with a focus on identifying and linking disease mentions within Medmentions ST21pv datasets.
To use this model, you need to set up a virtual environment and the inference code.
Start by cloning our ANGEL GitHub repository.
Then, run the following script to set up the environment:
bash script/environment/set_environment.sh
Then, if you want to run the model on a single sample, no preprocessing is required.
Simply execute the run_sample.sh script:
bash script/inference/run_sample.sh mm
To modify the sample with your own example, refer to the Direct Use section in our GitHub repository.
If you're interested in training or evaluating the model, check out the Fine-tuning section and Evaluation section.
Training
Training Data
The model was trained on the Medmentions ST21pv dataset, which includes annotated disease entities.
Training Procedure
Positive-only Pre-training: Initial training using only positive examples, following the standard approach.
Negative-aware Training: Subsequent training incorporated negative examples to improve the model's discriminative capabilities.
Evaluation
Testing Data
The model was evaluated using Medmentions ST21pv dataset.
Metrics
Accuracy at Top-1 (Acc@1): Measures the percentage of times the model's top prediction matches the correct entity.
Scores
Dataset
BioSYN (Sung et al., 2020)
SapBERT (Liu et al., 2021)
GenBioEL (Yuan et al., 2022b)
ANGEL (Ours)
Medmentions ST21pv
-
-
70.7
73.3
The scores of GenBioEL were reproduced.
Also we excluded the performance of BioSYN and SapBERT, as they were evaluated separately on the chemical and disease subsets, differing from our settings.
Citation
If you use the ANGEL_mm model, please cite:
bibtex
1@article{kim2024learning,
2 title={Learning from Negative Samples in Generative Biomedical Entity Linking},
3 author={Kim, Chanhwi and Kim, Hyunjae and Park, Sihyeon and Lee, Jiwoo and Sung, Mujeen and Kang, Jaewoo},
4 journal={arXiv preprint arXiv:2408.16493},
5 year={2024}
6}
