Honors Projects


This project analyzed the base and amino acid interactions and annotations through the use of unsupervised and supervised learning techniques. For unsupervised learning, clustering found the data was not able to be distinguished into clear groups which matched the original annotations through kmeans clustering and hierarchical clustering. For supervised learning, the use of random forest, glmnet, and deep learning neural networks were successful in creating accurate predictions. However, machine learning likely will not be able to replace the original complex program, but could be used for possible simplification.


Mathematics and Statistics



First Advisor

Dr. Craig Zirbel

First Advisor Department

Mathematics and Statistics

Second Advisor

Dr. Paul Morris

Second Advisor Department

Biological Sciences

Publication Date

Spring 4-26-2021