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• W2352816996 abstract "In order to develop better therapies to combat specific abnormalities present in the gene regulatory network (GRN) of cancer patients, it is crucial to gain a better understanding of regulatory networks in complex biological systems. An important class of methods in systems biology are network inference (NI) methods, which aim to reconstruct a GRN from high-throughput data (e.g. microarrays or next-generation sequencing). GENIE3 is a state-of-the-art method which employs feature selection to identify the best subset of regulators for each gene. While this method is amongst the best performing NI methods, it fails to take into account expected topological properties of a GRN: a GRN consists of modules, each of which consists of genes coregulated by a common set of regulators. We present BiGENIE, a method which takes the modular topology of a GRN into account. By firstly inferring modules – groups of genes coregulated by a common regulator – using several biclustering methods, the overall topology of the network is reconstructed. Subsequently, the regulator genes for each of the modules is inferred using GENIE3." @default.
• W2352816996 created "2016-06-24" @default.
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• W2352816996 date "2013-01-01" @default.
• W2352816996 modified "2023-09-26" @default.
• W2352816996 title "Network inference by integrating biclustering and feature selection" @default.
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