HCV genotyping using statistical classification approachReport as inadecuate

HCV genotyping using statistical classification approach - Download this document for free, or read online. Document in PDF available to download.

Journal of Biomedical Science

, 16:62

First Online: 08 July 2009Received: 01 May 2009Accepted: 08 July 2009DOI: 10.1186-1423-0127-16-62

Cite this article as: Qiu, P., Cai, XY., Ding, W. et al. J Biomed Sci 2009 16: 62. doi:10.1186-1423-0127-16-62


The genotype of Hepatitis C Virus HCV strains is an important determinant of the severity and aggressiveness of liver infection as well as patient response to antiviral therapy. Fast and accurate determination of viral genotype could provide direction in the clinical management of patients with chronic HCV infections. Using publicly available HCV nucleotide sequences, we built a global Position Weight Matrix PWM for the HCV genome. Based on the PWM, a set of genotype specific nucleotide sequence -signatures- were selected from the 5- NCR, CORE, E1, and NS5B regions of the HCV genome. We evaluated the predictive power of these signatures for predicting the most common HCV genotypes and subtypes. We observed that nucleotide sequence signatures selected from NS5B and E1 regions generally demonstrated stronger discriminant power in differentiating major HCV genotypes and subtypes than that from 5- NCR and CORE regions. Two discriminant methods were used to build predictive models. Through 10 fold cross validation, over 99% prediction accuracy was achieved using both support vector machine SVM and random forest based classification methods in a dataset of 1134 sequences for NS5B and 947 sequences for E1. Prediction accuracy for each genotype is also reported.

Electronic supplementary materialThe online version of this article doi:10.1186-1423-0127-16-62 contains supplementary material, which is available to authorized users.

Download fulltext PDF

Author: Ping Qiu - Xiao-Yan Cai - Wei Ding - Qing Zhang - Ellie D Norris - Jonathan R Greene

Source: https://link.springer.com/

Related documents