2018
DOI: 10.1101/260018
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Gene Coregulation and Coexpression in the Aryl Hydrocarbon Receptor-mediated Transcriptional Regulatory Network in the Mouse Liver

Abstract: Tissue-specific network models of chemical-induced gene perturbation can improve our mechanistic understanding of the intracellular events leading to adverse health effects resulting from chemical exposure. The aryl hydrocarbon receptor (AHR) is a ligand-inducible transcription factor (TF) that activates

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Cited by 2 publications
(1 citation statement)
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“…For example, using over 600 microarray experiments on S. cerevisiae , Allocco et al showed that the correlation ( r ) between any two genes’ expression profile must be over r = 0.84 in order to have a 50% chance of sharing the same regulation by a given transcription factor [57]. Thus, beyond identifying strong correlation between genes’ expression profiles, construction of co-regulation networks requires incorporation of additional regulatory information, which can be provided, for instance, by transcription factor (TF) binding analysis either using in silico databases [58], or including splicing information [59] or chromatin immunoprecipitation (ChIP) data [60]. For example, Ding et al incorporated protein interactions and TF binding motif data to construct gene regulatory networks, identifying ATF1 (Activating Transcription Factor 1), which regulates microglia anti-inflammatory action in AD through the cell surface marker CXCR4 [61,62].…”
Section: Transcriptomic Network To Understand Functional Gene-genmentioning
confidence: 99%
“…For example, using over 600 microarray experiments on S. cerevisiae , Allocco et al showed that the correlation ( r ) between any two genes’ expression profile must be over r = 0.84 in order to have a 50% chance of sharing the same regulation by a given transcription factor [57]. Thus, beyond identifying strong correlation between genes’ expression profiles, construction of co-regulation networks requires incorporation of additional regulatory information, which can be provided, for instance, by transcription factor (TF) binding analysis either using in silico databases [58], or including splicing information [59] or chromatin immunoprecipitation (ChIP) data [60]. For example, Ding et al incorporated protein interactions and TF binding motif data to construct gene regulatory networks, identifying ATF1 (Activating Transcription Factor 1), which regulates microglia anti-inflammatory action in AD through the cell surface marker CXCR4 [61,62].…”
Section: Transcriptomic Network To Understand Functional Gene-genmentioning
confidence: 99%