Date of Award

5-2019

Document Type

Thesis open access

Department

Computer Science

First Advisor

Matthew A. Hibbs

Second Advisor

Mark Lewis

Third Advisor

Kevin Livingstone

Abstract

HIV is a chronic and debilitating disease affecting the lives of millions of people globally. While therapies to treat HIV are available, drug resistance is a consistent problem. For this reason, an effective means of determining drug resistance for a given isolate is needed. In this experiment, we use a simple Artificial Neural Network (ANN) model trained on phenotypically labeled sequences from HIVdb for resistance classifications. We also observe an interesting data processing method, and determine train and test set division before such data processing is optimal for network performance.

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