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Georgia State University

Theses/Dissertations

2020

Machine learning

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Integrated Study Of Liver Fibrosis: Modeling And Clinical Detection, Hao Chen Aug 2020

Integrated Study Of Liver Fibrosis: Modeling And Clinical Detection, Hao Chen

Mathematics Dissertations

The liver is a vital organ that carries out over 500 essential tasks, including fat metabolism, blood filtering, bile production, and some protein production. Although the structure of the liver and the role of each type of cells in the liver are well known, the biomedical and mechanical interplays within liver tissues remain unclear. Chronic liver diseases are a significant public health challenge. All chronic liver diseases lead to liver fibrosis due to excessive fiber accumulation, resulting in cirrhosis and loss of liver function. Only early stage liver fibrosis is reversible. However, early-stage liver fibrosis is difficult to diagnose. How …


Identifying Influential Variables In The Prediction Of Type 2 Diabetes Using Machine Learning Methods, Amanda E. Chernishkin Aug 2020

Identifying Influential Variables In The Prediction Of Type 2 Diabetes Using Machine Learning Methods, Amanda E. Chernishkin

Public Health Theses

This study investigates three alternative machine learning methods to explore influential predictors of type 2 diabetes. It compares ridge, lasso, and elastic net regression to linear regression, and focuses on 12 outcome variables that include age, sex, race, income, education level, body mass index, waist circumference, arm circumference, hip circumference, family history, smoking status, sleep duration, high blood pressure, and high-density lipoprotein. Ridge, lasso and elastic net regression do not outperform linear regression but do assist in choosing a simpler model which could be important for improving future modeling.


Machine Learning And Deep Learning To Predict Cross-Immunoreactivity Of Viral Epitopes, Zahra Tayebi May 2020

Machine Learning And Deep Learning To Predict Cross-Immunoreactivity Of Viral Epitopes, Zahra Tayebi

Computer Science Theses

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 …