Arbeitspapier / Forschungsbericht | Open Access

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

Autor(en)
ter Horst, Hendrik ; Hartung, Matthias ; Klinger, Roman ; Zwick, Matthias ; Cimiano, Philipp 0000-0002-4771-441X
Verlag
Bielefeld University 
URN
urn:nbn:de:0070-pub-29140398
URL
https://pub.uni-bielefeld.de/record/2914039
Arxiv
https://arxiv.org/abs/1709.09239

Weitere Details

Sprache
englisch
Darstellungsform
Text
Schlagwörter
biomedical literature , disease-gene associations , graph-based features , machine learning , text mining
Link zum Volltext (intern)
https://pub.uni-bielefeld.de/record/2914039
DDC
000

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.

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