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• W4318896523 abstract "Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes.To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall.https://github.com/chaofen123/WMDS.net.Supplementary data are available at Bioinformatics online." @default.
• W4318896523 created "2023-02-02" @default.
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• W4318896523 date "2023-02-01" @default.
• W4318896523 modified "2023-10-18" @default.
• W4318896523 title "WMDS.net: a network control framework for identifying key players in transcriptome programs" @default.
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• W4318896523 doi "https://doi.org/10.1093/bioinformatics/btad071" @default.
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• W4318896523 hasPublicationYear "2023" @default.
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