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.
DOI
https://doi.org/10.57709/17590377
Recommended Citation
Tayebi, Zahra, "Machine Learning and Deep Learning to Predict Cross-immunoreactivity of Viral Epitopes." Thesis, Georgia State University, 2020.
doi: https://doi.org/10.57709/17590377
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