Model Based Framework for Estimating Mutation Rate of Hepatitis C Virus in Egypt

2013/03/21 by Nabila Shikoun, Mohamed El Nahas, Shikoun, Nabila +4
Computer Science · Mathematics · Medicine · #Artificial Intelligence (cs.AI) #Artificial intelligence #Biology #Computational biology #Computer science #FOS: Computer and information sciences #Gene #Genetics #Genotype #Hepacivirus #Hepatitis B Virus Studies #Hepatitis C virus #Hepatitis C virus research #Hidden Markov model #Liver Disease Diagnosis and Treatment #Markov chain #Markov model #Mathematics #Mutation #Mutation rate #NS5B #Statistics #Virology #Virus #cs.AI

paper · pdf · doi:10.48550/arxiv.1303.5177

published in arXiv (Cornell University) (Cornell University) · 6 pages, 5 figures

arxiv created 2013/03/21 · openalex publication_date 2013/03/21 · arxiv updated 2013/03/22 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences

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