Clinical Trials Commons^™

Machine Learning Methods For Prediction Of Human Infectious Virus And Imputation Of Hla Alleles, Xiaoqing Gao

Dissertations, Master's Theses and Master's Reports

This dissertation contains three Chapters. The following is a concise description of each Chapters.

In Chapter 1, we introduced the Random Forest, a machine learning method, to foresee whether a virus is capable of infecting humans. The Covid pandemic informs us the importance of predicting the ability of a zoonotic virus that can infect humans from its genomic sequence. We used the -mer with and as features of a virus to predict if it can affect humans. We further employed the Boruta algorithm to select the important features, then fed those important features into the Random Forest method to train …

Analysis Of Data From A Study To Identify Potential Biomarkers To Indicate Renal Injury, Mitchell D. Tahtinen

Dissertations, Master's Theses and Master's Reports

Ureteropelvic junction obstruction is a disease in which flow from the kidney to the bladder is obstructed for extended periods of time causing irreversible damage to the kidney. Current tests to detect kidney damage caused by obstruction are not effective until significant damage occurs. The purpose of this report is to identify a panel of biomarkers in urine to detect kidney damage earlier by analyzing data collected from a two-part study. Currently, two established urinary biomarkers to indicate kidney damage are NGAL and KIM-1. Biomarkers of interest in this study are CD13, CD10, and CD26. Results from the linear mixed …