BACKGROUND: Discovering novel disease genes is still challenging for diseases 
for which no prior knowledge--such as known disease genes or disease-related 
pathways--is available. Performing genetic studies frequently results in large 
lists of candidate genes of which only few can be followed up for further 
investigation. We have recently developed a computational method for 
constitutional genetic disorders that identifies the most promising candidate 
genes by replacing prior knowledge by experimental data of differential gene 
expression between affected and healthy individuals.To improve the performance 
of our prioritization strategy, we have extended our previous work by applying 
different machine learning approaches that identify promising candidate genes by 
determining whether a gene is surrounded by highly differentially expressed 
genes in a functional association or protein-protein interaction network.
RESULTS: We have proposed three strategies scoring disease candidate genes 
relying on network-based machine learning approaches, such as kernel ridge 
regression, heat kernel, and Arnoldi kernel approximation. For comparison 
purposes, a local measure based on the expression of the direct neighbors is 
also computed. We have benchmarked these strategies on 40 publicly available 
knockout experiments in mice, and performance was assessed against results 
obtained using a standard procedure in genetics that ranks candidate genes based 
solely on their differential expression levels (Simple Expression Ranking). Our 
results showed that our four strategies could outperform this standard procedure 
and that the best results were obtained using the Heat Kernel Diffusion Ranking 
leading to an average ranking position of 8 out of 100 genes, an AUC value of 
92.3% and an error reduction of 52.8% relative to the standard procedure 
approach which ranked the knockout gene on average at position 17 with an AUC 
value of 83.7%.
CONCLUSION: In this study we could identify promising candidate genes using 
network based machine learning approaches even if no knowledge is available 
about the disease or phenotype.