This is the implementation of our NAACL 2022 paper:
Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation
Junwei Yang, Zequn Liu, Ming Zhang* and Sheng Wang*
https://openreview.net/pdf?id=SLQlZl3bHbc
Please cite our paper if you use this code.
Our Pathway2Text dataset is released at https://zenodo.org/record/6510039#%23.Ym9F15NBz0o. Download mapping_database_to_pathway2text.json and pathway2text.json, put them in ./finaldata/.
Our model with best performance is available at https://drive.google.com/file/d/1Whn9oZ0hIfOly0lIOBEPVafMn_CxSaly/view?usp=sharing. Download and put all the parameters in ./params/.
- For Graph2Text ：
python graphtranswithdes.py --node-feat='labeldes' --used-part='graphdes'
- For Text2Graph node classification:
python nodeclassification.py --chosen-class='SIMPLE_CHEMICAL' --use-graph-des
Set --chosen-class='SIMPLE_CHEMICAL', 'MACROMOLECULE_MULTIMER' or 'MACROMOLECULE' for applying experiments on nodes of different type.
- For Text2Graph link prediction:
python linkprediction.py --use-graph-des --multiedge
The intermediate results are cached in the following paths:
./tokens/ --- tokenized sequences for node label, node des. and graph des
./embeddings/ --- [graph_des_embeddings, node_label_embeddings, node_des_embeddings] encoded by PLMs
./result/ --- generated descriptions for test graphs