Predicting Disease-Gene Associations using Cross-Document Graph-based Features

TL;DR

This paper introduces a graph-based machine learning method leveraging cross-document features to improve the accuracy of predicting disease-gene associations from text, surpassing simple co-occurrence approaches.

Contribution

It presents a novel RDF graph representation of disease-gene and gene-gene interactions combined with a classifier to distinguish valid associations from spurious ones.

Findings

30-point F1 score improvement over baseline

Effective filtering of spurious associations

Enhanced detection of relevant disease-gene links

Abstract

In the context of personalized medicine, text mining methods pose an interesting option for identifying disease-gene associations, as they can be used to generate novel links between diseases and genes which may complement knowledge from structured databases. The most straightforward approach to extract such links from text is to rely on a simple assumption postulating an association between all genes and diseases that co-occur within the same document. However, this approach (i) tends to yield a number of spurious associations, (ii) does not capture different relevant types of associations, and (iii) is incapable of aggregating knowledge that is spread across documents. Thus, we propose an approach in which disease-gene co-occurrences and gene-gene interactions are represented in an RDF graph. A machine learning-based classifier is trained that incorporates features extracted from the graph to separate disease-gene pairs into valid disease-gene associations and spurious ones. On the manually curated Genetic Testing Registry, our approach yields a 30 points increase in F1 score over a plain co-occurrence baseline.