Model Based Framework for Estimating Mutation Rate of Hepatitis C Virus in Egypt
This work addresses the challenge of modeling HCV mutation rates for improved detection and evolution prediction, but it appears incremental as it applies existing methods to a specific dataset without novel methodological breakthroughs.
The paper tackled the problem of estimating the mutation rate of Hepatitis C virus (HCV) in Egypt by developing a model-based framework using profile hidden Markov models and pair-wise distance methods, applied to a dataset of HCV genotype 4 subtype a in the NS5B zone, but no concrete numerical results or performance metrics were reported.
Hepatitis C virus (HCV) is a widely spread disease all over the world. HCV has very high mutation rate that makes it resistant to antibodies. Modeling HCV to identify the virus mutation process is essential to its detection and predicting its evolution. This paper presents a model based framework for estimating mutation rate of HCV in two steps. Firstly profile hidden Markov model (PHMM) architecture was builder to select the sequences which represents sequence per year. Secondly mutation rate was calculated by using pair-wise distance method between sequences. A pilot study is conducted on NS5B zone of HCV dataset of genotype 4 subtype a (HCV4a) in Egypt.