2018
DOI: 10.1101/254730
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Capturing context-specific regulation in molecular interaction networks

Abstract: Motivation: Gene expression changes over time in response to perturbations. These changes are coordinated into functional modules via regulatory interactions. The genes within a functional module are expected to be differentially expressed in a manner coherent with their regulatory network. This perspective presents a promising approach to increase power to detect differential signals as well as for describing regulated modules from a mechanistic point of view. Results: We present an effective procedure for id… Show more

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Cited by 4 publications
(3 citation statements)
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“…In the last decade, several methodologies have been developed to define and identify core regulatory networks, disease modules or context-specific subnetworks from large molecular interaction network ( Khan et al, 2018 ; Park et al, 2019 ; Rush and Repsilber, 2018 ; Dreyer et al, 2018 , Jaitly et al, 2020 , Saelens et al, 2018 , Singh et al, 2020 ). The AIR allows interfacing with such approaches in general through additional plugins.…”
Section: Discussionmentioning
confidence: 99%
“…In the last decade, several methodologies have been developed to define and identify core regulatory networks, disease modules or context-specific subnetworks from large molecular interaction network ( Khan et al, 2018 ; Park et al, 2019 ; Rush and Repsilber, 2018 ; Dreyer et al, 2018 , Jaitly et al, 2020 , Saelens et al, 2018 , Singh et al, 2020 ). The AIR allows interfacing with such approaches in general through additional plugins.…”
Section: Discussionmentioning
confidence: 99%
“…In the last decade, several methodologies have been developed to define and identify core regulatory network, disease modules, context-specific subnetworks from large molecular interaction network [44][45][46][47] . The AIR allows interfacing with such approaches in general through additional plugins.…”
Section: The Identification Of Core Regulatory Processesmentioning
confidence: 99%
“…Network-based algorithms predict drug or disease targets by combining network information and transcriptomic data [14, 21-27]. Two recent representatives, DeMAND [22] and ProTINA [14], model the systemic dysregulation of regulatory network caused by a drug treatment, connecting molecular interactions with differential expression (DE).…”
Section: Introductionmentioning
confidence: 99%