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

ter Horst H, Hartung M, Klinger R, Zwick M, Cimiano P (2016)
Bielefeld University.

Download
OA 496.90 KB
Working Paper | Published | English
Author
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 F 1 score over a plain co-occurrence baseline.
Publishing Year
PUB-ID

Cite this

ter Horst H, Hartung M, Klinger R, Zwick M, Cimiano P. Predicting Disease-Gene Associations using Cross-Document Graph-based Features. Bielefeld University; 2016.
ter Horst, H., Hartung, M., Klinger, R., Zwick, M., & Cimiano, P. (2016). Predicting Disease-Gene Associations using Cross-Document Graph-based Features. Bielefeld University.
ter Horst, H., Hartung, M., Klinger, R., Zwick, M., and Cimiano, P. (2016). Predicting Disease-Gene Associations using Cross-Document Graph-based Features. Bielefeld University.
ter Horst, H., et al., 2016. Predicting Disease-Gene Associations using Cross-Document Graph-based Features, Bielefeld University.
H. ter Horst, et al., Predicting Disease-Gene Associations using Cross-Document Graph-based Features, Bielefeld University, 2016.
ter Horst, H., Hartung, M., Klinger, R., Zwick, M., Cimiano, P.: Predicting Disease-Gene Associations using Cross-Document Graph-based Features. Bielefeld University (2016).
ter Horst, Hendrik, Hartung, Matthias, Klinger, Roman, Zwick, Matthias, and Cimiano, Philipp. Predicting Disease-Gene Associations using Cross-Document Graph-based Features. Bielefeld University, 2016.
All files available under the following license(s):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Main File(s)
File Name
paper.pdf 496.90 KB
Access Level
OA Open Access
Last Uploaded
2017-09-28T07:58:34Z

This data publication is cited in the following publications:
This publication cites the following data publications:

Export

0 Marked Publications

Open Data PUB

Sources

arXiv https://arxiv.org/abs/1709.09239

Search this title in

Google Scholar