# Biomedical Information Extraction for Disease Gene Prioritization

arXiv: https://arxiv.org/abs/2011.05188 · HTML: https://mlsanity.com/paper/2011.05188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Comparison of biomedical relationship extraction methods and models for knowledge graph creation](https://mlsanity.com/paper/2201.01647/) (2022)
- [Scalable Construction of a Lung Cancer Knowledge Base: Profiling Semantic Reasoning in LLMs](https://mlsanity.com/paper/2601.02604/) (2026)

## Abstract (arXiv)

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.