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

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 .

Authors

Institutions

Publication Details

Journal
Journal of Health and Biomedical Informatics
Published
2025-09-22
DOI
https://doi.org/10.34172/jhbmi.2025.17
Primary Topic
Hepatitis C virus research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Elham Mousavi, Majid Nikoubin‐Boroujeni, Zahra Arab‐Bafrani
Journal of Health and Biomedical Informatics
Hepatitis C virus research
article

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

Elham Mousavi, Majid Nikoubin‐Boroujeni, Zahra Arab‐Bafrani
article en

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 .

Journal of Health and Biomedical InformaticsVol. 12(2)
Golestan University (IR), Kerman University of Medical Sciences (IR), Islamic Azad University of Gorgan (IR), Golestan University of Medical Sciences (IR)
Good health and well-being
Openalex Percentile: Top 26%
Hepatitis C virus research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.