Work

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

Year: 2025

Type: article

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.

Source: Scientific Reports

DOI: 10.1038/s41598-025-98366-5

Authors: J. P. Correia, L. R. da Silva, R. Silva

Institutions: Universidade Federal do Rio Grande do Norte, Fundação Getulio Vargas, Centro Brasileiro de Pesquisas Físicas, Instituto Nacional de Ciência e Tecnologia de Sistemas Complexos, Universidade do Estado do Rio Grande do Norte

Language: English

Topic: Fractal and DNA sequence analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690