Profile Hidden Markov Model for Detection and Prediction of Hepatitis C Virus Mutation
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 of HCV based on profile hidden
Markov model (PHMM) architecture. An iterative model
learning procedure is proposed and applied to both full-length
sequence of virus and its very high variation (mutation) zone
called NS5A. A pilot study on HCV dataset of type 4 is
conducted which is of special concern in Egypt
Keywords: Hepatitis C virus (HCV), Profile Hidden Markov Model (PHMM), Non-structure 5 A(NS5A)
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ABOUT THE AUTHORS
Mohamed El Nahas
Prof. of Pattern Recognition Faculty of Engineering, Al Azhar University, Nasr city, Cairo, Egypt
Samar Kassim
Prof. of Medical Biochemistry & molecular Biology Faculty of Medicine, Ain Shams University Abbassia, Cairo, Egypt
Nabila Shikoun
PHD student Faculty of Engineering, Al Azhar University, Nasr city, Cairo, Egypt
Mohamed El Nahas
Prof. of Pattern Recognition Faculty of Engineering, Al Azhar University, Nasr city, Cairo, Egypt
Samar Kassim
Prof. of Medical Biochemistry & molecular Biology Faculty of Medicine, Ain Shams University Abbassia, Cairo, Egypt
Nabila Shikoun
PHD student Faculty of Engineering, Al Azhar University, Nasr city, Cairo, Egypt
