NDLI: Host-specific HCV evolution and response to the combined interferon and ribavirin therapy

Content Provider	IEEE Xplore Digital Library
Author	Lara, J. Tavis, J.E. Donlin, M.J. Lee, W.M. He-Jun Yuan Pearlman, B.L. Vaughan, G. Forbi, J.C. Guo-liang Xia Khudyakov, Y.E.
Copyright Year	2011
Description	Author affiliation: Center for Hepatitis C, Atlanta Medical, Center, Atlanta, GA (Pearlman, B.L.) \|\| Divisions of Digestive and Liver Disease and Infectious Diseases, UT Southwestern Medical Center at Dallas, Dallas, TX and (Lee, W.M.; He-Jun Yuan) \|\| Department of Molecular Microbiology and Immunology (Tavis, J.E.; Donlin, M.J.) \|\| Molecular Epidemiology & Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, GA (Lara, J.; Vaughan, G.; Forbi, J.C.; Guo-liang Xia; Khudyakov, Y.E.)
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.
Starting Page	102
Ending Page	109
File Size	950125
Page Count	8
File Format	PDF
ISBN	9781457716126
e-ISBN	9781457716133
DOI	10.1109/BIBMW.2011.6112361
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Medical treatment Genomics Predictive models Bioinformatics Immune system
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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