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Journal of Microscopy
Article . 2013 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Journal of Microscopy
Article
License: CC BY
Data sources: UnpayWall
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PubMed Central
Other literature type . 2013
License: CC BY
Data sources: PubMed Central
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Inferring signalling networks from images

Authors: Evans, L; Sailem, H; Vargas, P Pascual; Bakal, C;

Inferring signalling networks from images

Abstract

SummaryThe mapping of signalling networks is one of biology's most important goals. However, given their size, complexity and dynamic nature, obtaining comprehensive descriptions of these networks has proven extremely challenging. A fast and cost‐effective means to infer connectivity between genes on a systems‐level is by quantifying the similarity between high‐dimensional cellular phenotypes following systematic gene depletion. This review describes the methodology used to map signalling networks using data generated in the context of RNAi screens.

Keywords

Microscopy, Cytological Techniques, Image Processing, Computer-Assisted, Animals, Invited Reviews, Drosophila, Gene Silencing, Cells, Cultured, Cell Physiological Phenomena, Signal Transduction

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Average
Green
hybrid