Evidence map›Paper›PMID 24947751›Full record

ArticleBioinformatics (Oxford, England)2014

Correcting for link loss in causal network inference caused by regulator interference.

Ying Wang, Christopher A Penfold, David A Hodgson, Miriam L Gifford, Nigel J Burroughs

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ying WangWarwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.
Christopher A PenfoldWarwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.
David A HodgsonWarwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.
Miriam L GiffordWarwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.
Nigel J BurroughsWarwick Systems Biology Centre and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.

Funding

Biotechnology and Biological Sciences Research Council BB/H019502/1Biotechnology and Biological Sciences Research Council BB/H109502/1
6 · The paper itself

Abstract

motivationThere are a number of algorithms to infer causal regulatory networks from time series (gene expression) data. Here we analyse the phenomena of regulator interference, where regulators with similar dynamics mutually suppress both the probability of regulating a target and the associated link strength; for instance, interference between two identical strong regulators reduces link probabilities by ∼50%.

resultsWe construct a robust method to define an interference-corrected causal network based on an analysis of the conditional link probabilities that recovers links lost through interference. On a large real network (Streptomyces coelicolor, phosphate depletion), we demonstrate that significant interference can occur between regulators with a correlation as low as 0.865, losing an estimated 34% of links by interference. However, levels of interference cannot be predicted from the correlation between regulators alone and are data specific. Validating against known networks, we show that high numbers of functional links are lost by regulator interference. Performance against other methods on DREAM4 data is excellent. AVAILABILITY AND IMPLEMENTATION: The method is implemented in R and is publicly available as the NIACS package at http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software.

Indexed as

Gene ExpressionGene Expression ProfilingAlgorithmsArabidopsisCircadian RhythmComputational BiologyGene Expression RegulationModels, StatisticalProbabilityProgramming LanguagesStreptomyces coelicolor

Identifiers

PMID24947751
PMCPMC4173021

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.