BACKGROUND: High-throughput molecular interaction data have been used 
effectively to prioritize candidate genes that are linked to a disease, based on 
the observation that the products of genes associated with similar diseases are 
likely to interact with each other heavily in a network of protein-protein 
interactions (PPIs). An important challenge for these applications, however, is 
the incomplete and noisy nature of PPI data. Information flow based methods 
alleviate these problems to a certain extent, by considering indirect 
interactions and multiplicity of paths.
RESULTS: We demonstrate that existing methods are likely to favor highly 
connected genes, making prioritization sensitive to the skewed degree 
distribution of PPI networks, as well as ascertainment bias in available 
interaction and disease association data. Motivated by this observation, we 
propose several statistical adjustment methods to account for the degree 
distribution of known disease and candidate genes, using a PPI network with 
associated confidence scores for interactions. We show that the proposed methods 
can detect loosely connected disease genes that are missed by existing 
approaches, however, this improvement might come at the price of more false 
negatives for highly connected genes. Consequently, we develop a suite called 
DADA, which includes different uniform prioritization methods that effectively 
integrate existing approaches with the proposed statistical adjustment 
strategies. Comprehensive experimental results on the Online Mendelian 
Inheritance in Man (OMIM) database show that DADA outperforms existing methods 
in prioritizing candidate disease genes.
CONCLUSIONS: These results demonstrate the importance of employing accurate 
statistical models and associated adjustment methods in network-based disease 
gene prioritization, as well as other network-based functional inference 
applications. DADA is implemented in Matlab and is freely available at 
http://compbio.case.edu/dada/.