Network analysis of differential expression using machine learning approaches

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

Using three of their own strategies to analyze gene expression scores versus Simple Expression Ranking, the 

authors found:

" 

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 in average at position 17 with an AUC value of 83.7%.

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http://www.biomedcentral.com/1471-2105/11/460/abstract

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