ArticleBioinformatics (Oxford, England)2016
Linear effects models of signaling pathways from combinatorial perturbation data.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Learning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects.Bioinformatics (Oxford, England) · 2019Article
- In Silico Knockout Screening ofBioMed research international · 2018Article
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2 authors.
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Abstract
motivationPerturbations constitute the central means to study signaling pathways. Interrupting components of the pathway and analyzing observed effects of those interruptions can give insight into unknown connections within the signaling pathway itself, as well as the link from the pathway to the effects. Different pathway components may have different individual contributions to the measured perturbation effects, such as gene expression changes. Those effects will be observed in combination when the pathway components are perturbed. Extant approaches focus either on the reconstruction of pathway structure or on resolving how the pathway components control the downstream effects.
resultsHere, we propose a linear effects model, which can be applied to solve both these problems from combinatorial perturbation data. We use simulated data to demonstrate the accuracy of learning the pathway structure as well as estimation of the individual contributions of pathway components to the perturbation effects. The practical utility of our approach is illustrated by an application to perturbations of the mitogen-activated protein kinase pathway in Saccharomyces cerevisiaeAvailability and Implementation: lem is available as a R package at http://www.mimuw.edu.pl/∼szczurek/lem CONTACT: szczurek@mimuw.edu.pl; niko.beerenwinkel@bsse.ethz.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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