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13 October 2015
Investigating Genotype 1a HCV Drug Resistance in NS5A Region via Bayesian Inference
Jing Zhang(
)
)
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Fu Y, Chen G, Fu L, et al.
Investigating Genotype 1a HCV Drug Resistance in NS5A Region via Bayesian Inference.
Tsinghua Science and Technology,
2015, 20(5): 484-490.
https://doi.org/10.1109/TST.2015.7297747
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Hepatitis C virus (HCV) treatment is on the cutting edge of medicine. Due to the high rate of mutations and low fidelity of HCV replication, resistant strains quickly become dominant in a viral population under the selection pressure of a drug. In this paper, we examined the drug resistance mechanism in the NS5A region of genotype 1a HCV virus by comparing the sequence data from interferon-ribavirin treated and untreated patients. To find the drug resistance difference, we used innovative Bayesian probability models to detect mutation combinations and inferred detailed interaction structures of these mutations. We aim to provide reference to drug design and mutation mechanism understanding through our work.
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Investigating Genotype 1a HCV Drug Resistance in NS5A Region via Bayesian Inference
Jing Zhang(
)
)
Abstract
Hepatitis C virus (HCV) treatment is on the cutting edge of medicine. Due to the high rate of mutations and low fidelity of HCV replication, resistant strains quickly become dominant in a viral population under the selection pressure of a drug. In this paper, we examined the drug resistance mechanism in the NS5A region of genotype 1a HCV virus by comparing the sequence data from interferon-ribavirin treated and untreated patients. To find the drug resistance difference, we used innovative Bayesian probability models to detect mutation combinations and inferred detailed interaction structures of these mutations. We aim to provide reference to drug design and mutation mechanism understanding through our work.
Keywords:
Bayesian model, Hepatitis C virus (HCV), drug resistance, NS5A
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Publication history
Received: 25 July 2015
Accepted: 06 August 2015
Published:
13 October 2015
Issue date: October 2015
Copyright
The author(s) 2015
Acknowledgements
Dr. Jing Zhang was supported by start-up funding and Sesseel Award from Yale University. The computation was done with the help from the Yale University Biomedical High Performance Computing Center, which was supported by the NIH grant RR19895.
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