The extraction of disease-gene associations from biomedical publications is a widely inves-

tigated field of research. In previous work, a frequent method was to implement natural

language processing tools that use semantic information to find such associations. How-

ever, most of these approaches are restricted to single documents. Retrieval systems that

predict novel associations across various documents often lack the ability to deal with the

huge amount of resulting candidates. In this work, we present a system that aggregates

information from a large corpora of scientific abstracts. This information is used to build a

comprehensive gene-interaction network, which is then used to predict novel disease-gene

associations. We tackle the problem of candidate reduction by integrating two separate

machine learning methods. We train a support vector machine to classify genes as disease

related or not and a support vector regression model to rank gene-candidates according to

their importance to a specific disease. Thereto, we make use of approved methods and ex-

tend them by a novel investigation of the gene-interaction network. In a model-evaluation

on two gold standards as well as in a case-study in cooperation with biomedical experts,

it is shown that the proposed methods are able to extract disease-gene-associations from

single documents and discover disease-related candidates across multiple documents.