Date of Award

5-8-2020

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Pavel Skums

Second Advisor

Dr. Alex Zelikovsky

Third Advisor

Dr. Robert W. Harrison

Abstract

Due to the poor understanding of features defining cross-immunoreactivity among heterogeneous epitopes, vaccine development against the hepatitis C virus (HCV) is trapped. The development of vaccines against HCV and human immunodeficiency virus, which are highly heterogeneous viruses (HIV) is significantly vulnerable due to variant-specific neutralizing immune responses. The novel vaccine strategies are based on some assumptions such as immunological specificity which is strongly linked to the epitope primary structure, by increasing genetic difference between epitopes cross-immunoreactivity (CR) will decline [1]. In this study first, we checked the hamming distance and statistic evaluation associating HVR1 sequence and CR based on the sequence of the immunogen and antigen pairs then generated five different machine learning models. Also, we implemented deep learning models like Convolutional Neural Network to predict CR. As a result, we could provide 90% accuracy by using the CNN model.

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