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Related Concept Videos

Hepatitis01:25

Hepatitis

Hepatitis is an inflammatory condition of the liver most commonly caused by hepatotropic viruses (A–E), though non-infectious causes such as alcohol and drugs also exist.Hepatitis AHepatitis A virus (HAV) is a non-enveloped RNA virus of the Picornaviridae family. It is primarily transmitted via the fecal-oral route, typically through ingestion of contaminated food or water. After ingestion, HAV enters the bloodstream through the oropharynx or intestinal epithelium and reaches the liver. The...

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Externally Validated Probabilistic Modeling of a Predefined Entecavir Resistance Pathway in HBV Using Independent

Christelos Kapatais1, Fanie Karaoulani2, Sotirios P Fortis3

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Summary

A new machine learning framework accurately identifies entecavir resistance in hepatitis B virus (HBV) polymerase sequences. This probabilistic approach offers a transparent and reproducible method for interpreting antiviral resistance, improving patient treatment strategies.

Keywords:
drug resistanceentecavirexternal validationhepatitis B virusmachine learningviral genomics

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Area of Science:

  • Virology and Molecular Biology
  • Machine Learning in Bioinformatics
  • Antiviral Drug Resistance

Background:

  • Accurate interpretation of hepatitis B virus (HBV) polymerase sequences is critical for detecting antiviral resistance, especially to high-barrier drugs like entecavir.
  • Current resistance detection methods are deterministic, lack uncertainty quantification, and are difficult to validate across different datasets.
  • There is a need for a transparent and reliable framework for reconstructing entecavir resistance pathways from HBV polymerase sequences.

Purpose of the Study:

  • To develop and externally validate a transparent probabilistic machine learning framework.
  • To reconstruct a predefined entecavir resistance pathway from HBV polymerase sequences.
  • To provide a standardized method for sequence-based resistance interpretation.

Main Methods:

  • Collected and curated HBV polymerase sequences from NCBI GenBank for development.
  • Indexed reverse transcriptase (RT) positions using motif-anchored numbering.
  • Defined entecavir resistance pathway using logistic regression with probability calibration, validated internally and externally on an independent HBVdb dataset.

Main Results:

  • The development dataset included 1174 sequences; 268 met the resistance pathway definition.
  • Internal validation showed perfect discrimination.
  • External validation on 11,513 sequences confirmed reproducible performance with preserved discrimination and stable thresholds, despite lower pathway prevalence (2.2%).

Conclusions:

  • A transparent, externally validated machine learning framework for probabilistic identification of entecavir resistance pathways in HBV was developed.
  • The framework offers a reproducible probabilistic formalization of genotypic resistance definitions.
  • This approach can serve as a methodological standard for sequence-based resistance interpretation in HBV.