Evidence map›Paper›PMID 37860105›Full record

ArticleBioinformatics advances2023

BioNAR: an integrated biological network analysis package in bioconductor.

Colin McLean, Anatoly Sorokin, Thomas Ian Simpson, James Douglas Armstrong, Oksana Sorokina

Abstract read
In one paragraph

Article in Bioinformatics advances, 2023. 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

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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Colin McLeanEdinburgh Cancer Research Centre, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom.
Anatoly SorokinBiological Systems Unit, Okinawa Institute of Science and Technology, Onna, Okinawa 904-0495, Japan.
Thomas Ian SimpsonSchool of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom.ORCID https://orcid.org/0000-0003-0495-7187
James Douglas ArmstrongSchool of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom.
Oksana SorokinaSchool of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom.ORCID https://orcid.org/0000-0002-6786-9945

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different sub-complexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analysing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone. Results: Here, we present the Bioconductor package BioNAR, which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the network's vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a protein's impact within multiple complexes, and enables estimation of the co-occurrence of metadata, i.e. diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms. Availability and implementation: The package is available from Bioconductor release 3.17: https://bioconductor.org/packages/release/bioc/html/BioNAR.html.

Identifiers

PMID37860105
PMCPMC10582516

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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.