RCOVID19: Recurrence-based SARS-CoV-2 features using chaos game representation.

Mohammad Hossein Olyaee, Jamshid Pirgazi, Khosrow Khalifeh, Alireza Khanteymoori

Abstract read

In one paragraph

Article in Data in brief, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing 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

3 citing papers in PubMed.

Microbial characterization based on multifractal analysis of metagenomes.Frontiers in cellular and infection microbiology · 2023
Article
Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network.Expert systems with applications · 2022
Article
Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making.Informatics in medicine unlocked · 2022
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.

Mohammad Hossein OlyaeeFaculty of Engineering, Department of Computer Engineering, University of Gonabad, Gonabad, Iran.

Jamshid PirgaziDepartment of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran.

Khosrow KhalifehDepartment of Biology, Faculty of Sciences, University of Zanjan, Zanjan, Iran.

Alireza KhanteymooriDepartment of Computer Engineering, University of Zanjan, Zanjan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic. It was first detected in China and was rapidly spread to other countries. Several thousands of whole genome sequences of SARS-CoV-2 have been reported and it is important to compare them and identify distinctive evolutionary/mutant markers. Utilizing chaos game representation (CGR) as well as recurrence quantification analysis (RQA) as a powerful nonlinear analysis technique, we proposed an effective process to extract several valuable features from genomic sequences of SARS-CoV-2. The represented features enable us to compare genomic sequences with different lengths. The provided dataset involves totally 18 RQA-based features for 4496 instances of SARS-CoV-2.

Indexed as

Chaos game representationCoordinate seriesNonlinear analysisRecurrence quantification analysisSARS-CoV-2

Identifiers

PMID32835040

DOI10.1016/j.dib.2020.106144

PMCPMC7411429

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.