We introduce a biomedical information extraction (IE) pipeline that extracts biological relationships from text and demonstrate that its components, such as named entity recognition (NER) and relation extraction (RE), outperform state-of-the-art in BioNLP. We apply it to tens of millions of PubMed abstracts to extract protein-protein interactions (PPIs) and augment these extractions to a biomedical knowledge graph that already contains PPIs extracted from STRING, the leading structured PPI database. We show that, despite already containing PPIs from an established structured source, augmenting our own IE-based extractions to the graph allows us to predict novel disease-gene associations with a 20% relative increase in hit@30, an important step towards developing drug targets for uncured diseases.
@article{arxiv.2011.05188,
title = {Biomedical Information Extraction for Disease Gene Prioritization},
author = {Jupinder Parmar and William Koehler and Martin Bringmann and Katharina Sophia Volz and Berk Kapicioglu},
journal= {arXiv preprint arXiv:2011.05188},
year = {2020}
}
Comments
4th Knowledge Representation and Reasoning Meets Machine Learning Workshop (KR2ML), at NeurIPS 2020