BACKGROUND: Candidate gene prioritization aims to identify promising new genes 
associated with a disease or a biological process from a larger set of candidate 
genes. In recent years, network-based methods - which utilize a knowledge 
network derived from biological knowledge - have been utilized for gene 
prioritization. Biological knowledge can be encoded either through the network's 
links or nodes. Current network-based methods can only encode knowledge through 
links. This paper describes a new network-based method that can encode knowledge 
in links as well as in nodes.
RESULTS: We developed a new network inference algorithm called the Knowledge 
Network Gene Prioritization (KNGP) algorithm which can incorporate both link and 
node knowledge. The performance of the KNGP algorithm was evaluated on both 
synthetic networks and on networks incorporating biological knowledge. The 
results showed that the combination of link knowledge and node knowledge 
provided a significant benefit across 19 experimental diseases over using link 
knowledge alone or node knowledge alone.
CONCLUSIONS: The KNGP algorithm provides an advance over current network-based 
algorithms, because the algorithm can encode both link and node knowledge. We 
hope the algorithm will aid researchers with gene prioritization.