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

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

Download
OA 496.90 KB
Diskussionspapier | Veröffentlicht | Englisch
Autor
Abstract / Bemerkung
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.
Erscheinungsjahr
PUB-ID

Zitieren

ter Horst H, Hartung M, Klinger R, Zwick M, Cimiano P. Predicting Disease-Gene Associations using Cross-Document Graph-based Features. Bielefeld: 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: 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: Bielefeld University.
ter Horst, H., et al., 2016. Predicting Disease-Gene Associations using Cross-Document Graph-based Features, Bielefeld: Bielefeld University.
H. ter Horst, et al., Predicting Disease-Gene Associations using Cross-Document Graph-based Features, Bielefeld: 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, Bielefeld (2016).
ter Horst, Hendrik, Hartung, Matthias, Klinger, Roman, Zwick, Matthias, and Cimiano, Philipp. Predicting Disease-Gene Associations using Cross-Document Graph-based Features. Bielefeld: Bielefeld University, 2016.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Volltext(e)
Name
496.90 KB
Access Level
OA Open Access
Zuletzt Hochgeladen
2017-09-28T07:58:34Z

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Quellen

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

Suchen in

Google Scholar