Evidence map›Paper›PMID 36360202›Full record

ArticleGenes2022

Membrane Clustering of Coronavirus Variants Using Document Similarity.

Péter Lehotay-Kéry, Attila Kiss

Open access · goldAbstract read
In one paragraph

Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 56% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

2 authors at 1 institution in 2 countries.

Péter Lehotay-KéryDepartment of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary.ORCID 0000-0002-0884-4297
Attila KissDepartment of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary.ORCID 0000-0001-8174-6194
Eötvös Loránd University · HU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models.

Indexed as

COVID-19PandemicsAlgorithmsCluster AnalysisComputational BiologyHumansbioinformaticsclusteringcoronavirusCOVIDDoc2Vecdocument similaritygenomemembrane computingMinHashP systems

Identifiers

PMID36360202
PMCPMC9689808
OpenAlexW4307945553

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.