# Towards extracting supporting information about predicted protein-protein interactions

## TL;DR

This paper introduces UPSITE, a tool that helps validate predicted protein-protein interactions by extracting supporting evidence from biomedical literature.

## Contribution

The novel contribution is a text mining tool that provides contextual and semantic evidence for predicted PPIs.

## Key findings

- UPSITE achieved an Fscore of 0.88 on the HPRD50 corpus for relation extraction.
- Semantic similarity measured with angular distance was statistically significant.
- Presenting annotations and similarity scores helps biologists interpret predicted PPIs.

## Abstract

One of the goals of relation extraction is to identify protein-protein interactions (PPIs) in biomedical literature. Current systems are capturing binary relations and also the direction and type of an interaction. Besides assisting in the curation PPIs into databases, there has been little real-world application of these algorithms. We describe UPSITE, a text mining tool for extracting evidence in support of a hypothesized interaction. Given a predicted PPI, UPSITE uses a binary relation detector to check whether a PPI is found in abstracts in PubMed. If it is not found, UPSITE retrieves documents relevant to each of the two proteins separately, and extracts contextual information about biological events surrounding each protein, and calculates semantic similarity of the two proteins to provide evidential support for the predicted PPI. In evaluations, relation extraction achieved an Fscore of 0.88 on the HPRD50 corpus, and semantic similarity measured with angular distance was found to be statistically significant. With the development of PPI prediction algorithms, the burden of interpreting the validity and relevance of novel PPIs is on biologists. We suggest that presenting annotations of the two proteins in a PPI side-by-side and a score that quantifies their similarity lessens this burden to some extent.