  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.
