The identification of genes involved in health and disease remains a challenge. 
We describe a bioinformatics approach, together with a freely accessible, 
interactive and flexible software termed Endeavour, to prioritize candidate 
genes underlying biological processes or diseases, based on their similarity to 
known genes involved in these phenomena. Unlike previous approaches, ours 
generates distinct prioritizations for multiple heterogeneous data sources, 
which are then integrated, or fused, into a global ranking using order 
statistics. In addition, it offers the flexibility of including additional data 
sources. Validation of our approach revealed it was able to efficiently 
prioritize 627 genes in disease data sets and 76 genes in biological pathway 
sets, identify candidates of 16 mono- or polygenic diseases, and discover 
regulatory genes of myeloid differentiation. Furthermore, the approach 
identified a novel gene involved in craniofacial development from a 2-Mb 
chromosomal region, deleted in some patients with DiGeorge-like birth defects. 
The approach described here offers an alternative integrative method for gene 
discovery.