A novel candidate disease genes prioritization method based on module partition and rank fusion.

摘要: Abstract Identifying disease genes is very important not only for better understanding of gene function and biological process but also human medical improvement. Many computational methods have been proposed based on the similarity between all known (seed genes) candidate in entire interaction network. Under hypothesis that potential disease-related should be near seed network are located same module with will contribute to prediction, three modularized prioritization algorithms (MCDGPAs) identify genes. MCDGPA divided into steps: partition, each disease-associated module, rank fusion global ranking. When applied prostate cancer breast network, significantly improves previous terms cross-validation pr...