MOTIVATION: Hunting disease genes is a problem of primary importance in 
biomedical research. Biologists usually approach this problem in two steps: 
first a set of candidate genes is identified using traditional positional 
cloning or high-throughput genomics techniques; second, these genes are further 
investigated and validated in the wet lab, one by one. To speed up discovery and 
limit the number of costly wet lab experiments, biologists must test the 
candidate genes starting with the most probable candidates. So far, biologists 
have relied on literature studies, extensive queries to multiple databases and 
hunches about expected properties of the disease gene to determine such an 
ordering. Recently, we have introduced the data mining tool ENDEAVOUR (Aerts et 
al., 2006), which performs this task automatically by relying on different 
genome-wide data sources, such as Gene Ontology, literature, microarray, 
sequence and more.
RESULTS: In this article, we present a novel kernel method that operates in the 
same setting: based on a number of different views on a set of training genes, a 
prioritization of test genes is obtained. We furthermore provide a thorough 
learning theoretical analysis of the method's guaranteed performance. Finally, 
we apply the method to the disease data sets on which ENDEAVOUR (Aerts et al., 
2006) has been benchmarked, and report a considerable improvement in empirical 
performance.
AVAILABILITY: The MATLAB code used in the empirical results will be made 
publicly available.