Evidence map›Paper›PMID 39451890›Full record

ArticleEntropy (Basel, Switzerland)2024

Simulating Weak Attacks in a New Duplication-Divergence Model with Node Loss.

Ruihua Zhang, Gesine Reinert

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Ruihua ZhangDepartment of Statistics, University of Oxford, 24-29 St. Giles', Oxford OX1 3LB, UK.ORCID 0009-0006-4439-4226
Gesine ReinertDepartment of Statistics, University of Oxford, 24-29 St. Giles', Oxford OX1 3LB, UK.

Funding

Engineering and Physical Sciences Research Council EP/R018472/1Engineering and Physical Sciences Research Council EP/T018445/1Engineering and Physical Sciences Research Council EP/V056883/1Engineering and Physical Sciences Research Council EP/W037211/1
6 · The paper itself

Abstract

A better understanding of protein-protein interaction (PPI) networks representing physical interactions between proteins could be beneficial for evolutionary insights as well as for practical applications such as drug development. As a statistical model for PPI networks, duplication-divergence models have been proposed, but they suffer from resulting in either very sparse networks in which most of the proteins are isolated, or in networks which are much denser than what is usually observed, having almost no isolated proteins. Moreover, in real networks, where a gene codes a protein, gene loss may occur. The loss of nodes has not been captured in duplication-divergence models to date. Here, we introduce a new duplication-divergence model which includes node loss. This mechanism results in networks in which the proportion of isolated proteins can take on values which are strictly between 0 and 1. To understand this new model, we apply strong and weak attacks to networks from duplication-divergence models with and without node loss, and compare the results to those obtained when carrying out similar attacks on two real PPI networks of

Indexed as

duplication–divergence modelgene lossprotein–protein interaction networksweak attack

Identifiers

PMID39451890
PMCPMC11507016

What OpenQuestion holds

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LicenceCC BY
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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.