Host-specific HCV evolution and response to the combined interferon and ribavirin therapy
DOI: 10.1109/BIBMW.2011.6112361 Conference: 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), Atlanta, GA, USA, November 12-15, 2011
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.
Conference Paper: The EQ framework for learning equivalence classes of Bayesian networks[Show abstract] [Hide abstract]
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