Disease-causing aberrations in the normal function of a gene define that gene as 
a disease gene. Proving a causal link between a gene and a disease 
experimentally is expensive and time-consuming. Comprehensive prioritization of 
candidate genes prior to experimental testing drastically reduces the associated 
costs. Computational gene prioritization is based on various pieces of 
correlative evidence that associate each gene with the given disease and suggest 
possible causal links. A fair amount of this evidence comes from high-throughput 
experimentation. Thus, well-developed methods are necessary to reliably deal 
with the quantity of information at hand. Existing gene prioritization 
techniques already significantly improve the outcomes of targeted experimental 
studies. Faster and more reliable techniques that account for novel data types 
are necessary for the development of new diagnostics, treatments, and cure for 
many diseases.