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• W2798570041 abstract "Abstract Gene regulatory networks describe the regulatory relationships among genes, and developing methods for reverse engineering these networks are an ongoing challenge in computational biology. The majority of the initially proposed methods for gene regulatory network discovery create a network of genes and then mine it in order to uncover previously unknown regulatory processes. More recent approaches have focused on inferring modules of co-regulated genes, linking these modules with regulator genes and then mining them to discover new molecular biology. In this work we analyze module-based network approaches to build gene regulatory networks, and compare their performance to the well-established single gene network approaches. In particular, we focus on the problem of linking genes with known regulatory genes. First, modules are created iteratively using a regression approach that links co-expressed genes with few regulatory genes. After the modules are built, we create bipartite graphs to identify a set of target genes for each regulatory gene. We analyze several methods for uncovering these modules and show that a variational Bayes approach achieves significant improvement with respect to previously used methods for module creation on both simulated and real data. We also perform a topological and gene set enrichment analysis and compare several module-based approaches to single gene network approaches where a graph is built from the gene expression profiles without clustering genes in modules. We show that the module-based approach with variational Bayes outperforms all other methods and creates regulatory networks with a significantly higher rate of enriched molecular pathways. The code is written in R and can be downloaded from https://github.com/mikelhernaez/linker ." @default.
• W2798570041 created "2018-05-07" @default.
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• W2798570041 date "2018-04-25" @default.
• W2798570041 modified "2023-10-16" @default.
• W2798570041 title "Comparison of single gene and module-based methods for modeling gene regulatory networks" @default.
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• W2798570041 doi "https://doi.org/10.1101/307884" @default.
• W2798570041 hasPublicationYear "2018" @default.
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