Coordinated evolution among hepatitis C virus genomic sites is coupled to host factors and resistance to interferon

James Lara, John E. Tavis, Maureen J. Donlin, William M. Lee, He Jun Yuan, Brian L. Pearlman, Gilberto Vaughan, Joseph C. Forbi, Guo Liang Xia, Yury E. Khudyakov

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Machine-learning methods in the form of Bayesian networks (BN), linear projection (LP) and self-organizing tree (SOT) models were used to explore association among polymorphic sites within the HVR1 and NS5a regions of the HCV genome, host demographic factors (ethnicity, gender and age) and response to the combined interferon (IFN) and ribavirin (RBV) therapy. The BN models predicted therapy outcomes, gender and ethnicity with accuracy of 90%, 90% and 88.9%, respectively. The LP and SOT models strongly confirmed associations of the HVR1 and NS5A structures with response to therapy and demographic host factors identified by BN. The data indicate host specificity of HCV evolution and suggest the application of these models to predict outcomes of IFN/RBV therapy.

Original languageEnglish (US)
Pages (from-to)213-224
Number of pages12
JournalIn Silico Biology
Volume11
Issue number5
DOIs
Publication statusPublished - 2012

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ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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