MCDGPA is proposed to identify disease-related genes. MCDGPA is divided into three steps: module partition, genes prioritization in each disease-associated module, and rank fusion for the global ranking. When applied to the prostate cancer and breast cancer network, MCDGPA significantly improves previous algorithms in terms of cross-validation and disease-related genes prediction. In addition, the improvement is robust to the selection of gene prioritization methods when implementing prioritization in each disease-associated module and module partition algorithms when implementing network partition. In this sense MCDGPA is a general framework that allows integrating many previous gene prioritization methods and improving predictive accuracy.
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Chen X, Yan GY, Liao XP (2010)
A novel candidate disease genes prioritization method based on module partition and rank fusion.
OMICS 4: 337-356.