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This analysis comprehensively compares methods for gene regulatory network inference submitted through the DREAM5 challenge. It demonstrates that integration of predictions from multiple methods shows the most robust performance across data sets.
Algorithms that integrate genome-wide copy number and gene expression data offer a promising way to uncover genes that drive the progression of cancers. The performance of ten software tools on simulated and real cancer datasets of different sizes is directly compared in this Analysis.
