World Journal of
Pharmaceutical and Life Sciences

An International Peer Reviewed Journal for Pharmaceutical and Life Sciences
An Official Publication of Society for Advance Healthcare Research (Reg. No. : 01/01/01/31674/16)
World Journal of Pharmaceutical and Life Sciences (WJPLS) has indexed with various reputed international bodies like : Google Scholar, Index Copernicus, Indian Science Publications, SOCOLAR, China, Cosmos Impact Factor, Research Bible, Fuchu, Tokyo. JAPAN, Scientific Indexing Services (SIS), Jour Informatics (Under Process), UDLedge Science Citation Index, Global Impact Factor (In Process), International Impact Factor Services, International Scientific Indexing, UAE, International Society for Research Activity (ISRA) Journal Impact Factor (JIF), Science Library Index, Dubai, United Arab Emirates, International Innovative Journal Impact Factor (IIJIF), Scientific Journal Impact Factor (SJIF), Eurasian Scientific Journal Index (ESJI), Indian citation Index (ICI), IFSIJ Measure of Journal Quality, International Scientific Indexing, UAE (ISI), Web of Science Group (Under Process), Directory of Research Journals Indexing, 

Abstract

IN-SILICO T-CELL EPITOPE PREDICTION TOOL OF HEPATITIS C VIRUS (HCV)

Auwalu Muttaka*, Abdul-Hamid Abubakar Zubair, Sani S. Usman, Kaushik Vicas

ABSTRACT

Hepatitis C Virus (HCV) is a ssRNA infectious microbe that affects and kills millions of people worldwide annually However, various part of the virus that can be recognized processed and dealt with by immune system, such as B-cells, T-cells or microphages are known as epitopes or antigenic determinants. Knowledge of these epitopes is invaluable for the research and development, of vaccine and drug design, that will eradicate and diminish this life threaten virus. There is an exponential increase in the expression of these epitopes regularly which make them difficult to be handled. To rectify and deal with this problem, a new computational method was developed to analyze such kind of large data with the help of support vector machine (SVM). This analytical method consists of training, testing, classifying and validation of both T-cell epitopes and non T-cell epitopes of hepatitis C virus (HCV). To improve the performance of this method, the data were divided into (70% and 30%), (80% and 20%) and (90% and 10%) of train and test respectively using non-epitopes as control. The accuracy of class of data with amino acids feature (polarity, acidity, alkalinity, aliphaticity, etc) and without amino acids features were noted. The result was obtained by taking the average of the %accuracy which indicates high performance and potentiality of this method.

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