Evidence map›Paper›PMID 40301538›Full record

ArticleScientific reports2025

Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants.

J P Correia, L R da Silva, R Silva

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

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

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

3 authors.

J P CorreiaDepartment of Theoretical and Experimental Physics, Federal University of Rio Grande do Norte, 59072-970, Natal-RN, Brazil. jonathan.pessoa@fisica.ufrn.br.
L R da SilvaDepartment of Theoretical and Experimental Physics, Federal University of Rio Grande do Norte, 59072-970, Natal-RN, Brazil.
R SilvaDepartment of Theoretical and Experimental Physics, Federal University of Rio Grande do Norte, 59072-970, Natal-RN, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a novel approach for the classification of coronavirus species and variants of SARS-CoV-2 using Chaos Game Representation (CGR) and 2D Multifractal Detrended Fluctuation Analysis (2D MF-DFA). By extracting fractal parameters from CGR images, we constructed a state space that effectively distinguishes different species and variants. Our method achieved [Formula: see text] accuracy in species classification, with a notable [Formula: see text] accuracy for SARS-CoV-2 variants despite their genetic similarities. Using a Support Vector Machine (SVM) as a classifier further enhanced the performance. This approach, which requires fewer steps than most existing methods, offers an efficient and effective tool for viral classification, with implications for bioinformatics, public health, and vaccine development.

Indexed as

CoronavirusSARS-CoV-2Support Vector MachineComputational BiologyCOVID-19FractalsHumansCGRCoronaviridaeFractalRNASVMViral

Identifiers

PMID40301538
PMCPMC12041560

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.