EIOFX-DT: Leveraging graph centrality metrics for feature extraction and classification of viral genetic sequences.

Amin Khodaei, Zahra Pourabbas, Fatemeh Hashem-Zadehdizajyekan, Erfan Esmaeili

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Article in Biotechnology reports (Amsterdam, Netherlands), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Amin KhodaeiFaculty of Electrical & Computer Engineering, University of Tabriz, Tabriz, Iran.

Zahra PourabbasDepartment of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

Fatemeh Hashem-ZadehdizajyekanDepartment of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

Erfan EsmaeiliDepartment of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many diseases have a genetic origin, and analyzing intracellular structures through genetic data yields specific features for the diagnosis and classification of viral disease samples. In this study, 30 types of viruses were analyzed using a graph-based approach on genetic data. Genetic data has been modeled in the form of genomic sequences at the nucleotide scale using the graph theory of complex networks concepts. Degree and eigenvector centrality metrics were employed to extract features. The decision tree was utilized as a machine learning classifier algorithm on the resulting feature space. The results, presented in the form of interpretable rules, enable the classification and identification of virus types from both a binary and multi-class perspective. The model achieved high accuracy and f1 score, which exceeded 99 % on >173,000 samples. Additionally, the feature extraction algorithm demonstrated robust performance across all datasets and classifiers.

Indexed as

Complex networkDecision treeEigen-vector centralityFeature extractionGenetic dataGraph

Identifiers

PMID41488416

DOI10.1016/j.btre.2025.e00939

PMCPMC12756633

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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.