Chemical-protein Interaction Extraction via ChemicalBERT and Attention Guided Graph Convolutional Networks in Parallel

Implementation of our paper titled "Chemical-protein Interaction Extraction via ChemicalBERT and Attention Guided Graph Convolutional Networks in Parallel", IEEE International Conference on Bioinformatics and Biomedicine, Biomedical and Health Informatics, 2020.

The model consists of ChemicalBERT and Attention Guided Graph Convolutional Networks (AGGCN) two parallel components. We pre-train BERT on large-scale chemical interaction corpora and re-define it as ChemicalBERT to generate high-quality contextual representation, and employ AGGCN to capture syntactic graph information of the sentence. Finally, the contextual representation and syntactic graph representation are merged into a fusion layer and then fed into the fully-connected softmax layer to extract CPIs.

The paper has been accepted by 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020).

Conference: December 16-19, 2020

The Program can be found on the Conference Website by clicking Program on the left hand menu.

See below for an overview of the model architecture:

Requirements

• Python 3 (tested on 3.6.10)

• PyTorch (tested on 1.3.1)

• CUDA (tested on 10.1.243)

• pytorch_pretrained_bert (tested on 0.6.1)

• botocore (tested on 1.12.189)

• tensorflow (tested on 1.15.0)

• boto3 (tested on 1.9.162)

• requests (tested on 2.22.0)

• numpy (tested on 1.19.1)

• tqdm (tested on 4.42.1)

Download Resources

• Model config and model weights

Evaluation

we have conducted experiments on the ChemProt corpus and DDIExtraction 2013 corpus

Testing on CPI extraction

python3 eval_cpi.py

Testing on DDI extraction. Before run it, please modify the configuration information under /utils/constant.py

python3 eval_ddi.py