Evidence map›Paper›PMID 25880651›Full record

ArticleGenome biology2015

An approach for determining and measuring network hierarchy applied to comparing the phosphorylome and the regulome.

Chao Cheng, Erik Andrews, Koon-Kiu Yan, Matthew Ung, Daifeng Wang, Mark Gerstein

Abstract read
In one paragraph

Article in Genome biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
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  6. Observational
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  8. Plants are intelligent, here's how.Annals of botany · 2020
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chao ChengDepartment of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA. chao.cheng@dartmouth.edu.
Erik AndrewsDepartment of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA. erik.h.andrews.med@dartmouth.edu.
Koon-Kiu YanProgram in Computational Biology and Bioinformatics, Yale University, 260 Whitney Avenue, New Haven, CT, 06520, USA. koon-kiu.yan@yale.edu.
Matthew UngDepartment of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA. matthew.ung@dartmouth.edu.
Daifeng WangProgram in Computational Biology and Bioinformatics, Yale University, 260 Whitney Avenue, New Haven, CT, 06520, USA. daifeng.wang@yale.edu.
Mark GersteinProgram in Computational Biology and Bioinformatics, Yale University, 260 Whitney Avenue, New Haven, CT, 06520, USA. mark.gerstein@yale.edu.

Funding

Resource ProjectU41HG007355 · NHGRI · UNIVERSITY OF WASHINGTON · PI WATERSTON, ROBERT H · 2013 to 2021
$20.8M
NHGRI NIH HHS U41 HG007355
6 · The paper itself

Abstract

Many biological networks naturally form a hierarchy with a preponderance of downward information flow. In this study, we define a score to quantify the degree of hierarchy in a network and develop a simulated-annealing algorithm to maximize the hierarchical score globally over a network. We apply our algorithm to determine the hierarchical structure of the phosphorylome in detail and investigate the correlation between its hierarchy and kinase properties. We also compare it to the regulatory network, finding that the phosphorylome is more hierarchical than the regulome.

Indexed as

GenomicsAlgorithmsGene Regulatory NetworksHumansModels, BiologicalPhosphorylationYeasts

Identifiers

PMID25880651
PMCPMC4404648

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Registered trials

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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.