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February 14, 2026Journal of Health and Biomedical Informatics0 citationsOpen Access

Identification of Key Oligonucleotide Fragments in the Hepatitis C Virus Genome in Evasion of Interferon Mechanisms Using Weighting Algorithms

ZAZahra Arab‐BafraniMNMajid Nikoubin‐BoroujeniEMElham Mousavi

Key Points

  • The aim is to identify short oligonucleotide fragments in the hepatitis C virus genome that contribute to evasion of interferon treatment.
  • Employed ten different weighting models to identify short oligonucleotide fragments.
  • Distinguished dinucleotide, trinucleotide, and tetranucleotide sequences among HCV-infected patients.
  • Used Rapid Miner software for data analysis of viral genotypes 1a, 1b, and 2b.
  • Compared therapeutic responses among patients resistant and sensitive to interferon treatment.
  • Identified several significant oligonucleotide fragments, including UU, UA, and UC among dinucleotide sequences.
  • Found key trinucleotide and tetranucleotide sequences: GUA, CUA, and CGUA showed notable differences.
  • These sequences are crucial in influencing the interferon response against viruses.

Abstract

Introduction: The presence of short oligonucleotide fragments that constitute the viral genome is unique, and the variation in their abundance among different species significantly influences the pathogenicity of viral strains, evasion of the immune system, and response to antiviral treatment. Data mining and the use of weighting models are effective methods for identifying these characteristic fragments. Given the importance of oligonucleotide fragments in the genomic sequence of the hepatitis C virus (HCV) concerning resistance to interferon treatment and evasion of the host's innate immune system, this study aims to identify these characteristic fragments using weighting models applied to the HCV genomic sequence. Method: In this study, we employed ten different weighting models for the first time to identify short oligonucleotide fragments, including distinguishing dinucleotide, trinucleotide, and tetranucleotide sequences, between two groups of individuals infected with HCV who exhibit different therapeutic responses to interferon (INF) treatment. Ten weighting algorithms were executed and evaluated based on the relative abundance of short oligonucleotide fragments constituting the HCV genome in genotypes 1a, 1b, and 2b from two groups of patients resistant and sensitive to interferon treatment using Rapid Miner software . Results: Several oligonucleotide fragments, including UU, UA, and UC among dinucleotide sequences, and GUA, CUA, and CGUA among trinucleotide and tetranucleotide sequences, showed significant differences between the two groups. Previous studies indicate that the presence of these sequences plays a crucial role in the interferon response against viruses. Conclusion: The results of this study suggest that employing various weighting models in data mining can help predict important genomic indicators of the virus that are effective in evading the host's immune system and antiviral treatments at early stages. These indicators can subsequently be targeted in experimental studies for drug and vaccine design .

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Cite This Study

Arab‐Bafrani et al. (2025) studied this question.

synapsesocial.com/papers/699010f22ccff479cfe57430https://doi.org/10.34172/jhbmi.2025.17
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