ArticleBMC bioinformatics2025
PRCFX-DT: a new graph-based approach for feature selection and classification of genomic sequences.
Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- EIOFX-DT: Leveraging graph centrality metrics for feature extraction and classification of viral genetic sequences.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- The era of "Infectious Diseases+" has arrived: multi-disciplinary integration in pediatric infectious disease prevention and control.Frontiers in pediatrics · 2026Review
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIn recent years, viral diseases have exhibited a significant incidence of infections and fatalities. The analysis of viral genomic sequences can be efficacious in evaluating the present and potentially forthcoming condition of viruses. Considering the importance of the internal structure of the cell and the nucleotide sequences within it, analyzing nucleotide sequences can provide a range of discussable features. On the other hand, it has been demonstrated that the use of graph algorithms and machine learning in the analysis and examination of virus samples and even viral variants can yield beneficial results.
resultsThis study proposes a novel approach that utilizes complex networks and probabilistic graph modeling methods to analyze viral genomic sequences for feature extraction. The proposed approach, which relies on the PageRank centrality algorithm, operates on codons that are associated with the nucleotide sequences. Experiments with machine learning algorithms were conducted on multiple datasets of viruses and various variants of coronavirus and influenza viruses. The use of a decision tree classifier model on the extracted distinguishing features enabled the differentiation of coronavirus samples from other samples. The high discriminative capability of the graph node centrality feature played a significant role in these experiments, establishing a meaningful connection with genetic concepts as well. The decision tree classifier applied on 173,228 genomic sequence samples originating from 30 distinct virus types, showed a remarkable accuracy rate of 99.73%.
conclusionThe proposed algorithm was successfully tested on several types of viruses, and the interpretability of the extracted features also enabled its structural analysis. The use of a graph-based approach on genetic features containing information about the internal structure of nucleotides yielded results that could be significant for the identification of any type of virus or specific viral variant.
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Registered trials
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.