Evidence map›Paper›PMID 24789562›Full record

ArticleJournal of the Royal Society, Interface2014

Topological properties of robust biological and computational networks.

Saket Navlakha, Xin He, Christos Faloutsos, Ziv Bar-Joseph

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
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  7. Review
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  15. Chaperome Networks - Redundancy and Implications for Cancer Treatment.Advances in experimental medicine and biology · 2020
    Review
  16. Review
  17. Article
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

4 authors.

Saket NavlakhaMachine Learning Department, Carnegie Mellon University, , Pittsburgh, PA 15213, USA.
Xin He
Christos Faloutsos
Ziv Bar-Joseph

Funding

Synapse Elimination during Development: Pruning Rates, Models, and DiseasesF32MH099784 · NIMH · CARNEGIE-MELLON UNIVERSITY · PI NAVLAKHA, SAKET · 2013 to 2014
$61k
NIMH NIH HHS F32 MH099784NIMH NIH HHS F32-MH099784
6 · The paper itself

Abstract

Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields.

Indexed as

Computer Communication NetworksGene DuplicationGene Regulatory NetworksSaccharomyces cerevisiaeSignal TransductionSystems Biologybiological networkscommunication networksrobustnesssecurity

Identifiers

PMID24789562
PMCPMC4032542

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.