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• W2897069326 abstract "Gene regulatory networks control the cellular phenotype by changing the RNA and protein composition. Despite its importance, the gene regulatory network in higher organisms is only partly mapped out. Here, we investigate the potential of reverse engineering methods to unravel the structure of these networks. Particularly, we focus on modular response analysis (MRA), a method that can disentangle networks from perturbation data. We benchmark a version of MRA that was previously successfully applied to reconstruct a signalling-driven genetic network, termed MLMSMRA, to test cases mimicking various aspects of gene regulatory networks. We then investigate the performance in comparison with other MRA realisations and related methods. The benchmark shows that MRA has the potential to predict functional interactions, but also shows that successful application of MRA is restricted to small sparse networks and to data with a low signal-to-noise ratio." @default.
• W2897069326 created "2018-10-26" @default.
• W2897069326 creator A5078717142 @default.
• W2897069326 creator A5084856882 @default.
• W2897069326 date "2018-10-12" @default.
• W2897069326 modified "2023-10-03" @default.
• W2897069326 title "Reverse engineering gene regulatory networks by modular response analysis – a benchmark" @default.
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• W2897069326 doi "https://doi.org/10.1042/ebc20180012" @default.
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