Physical Sciences and Mathematics Commons^™

Unsupervised And Supervised Learning For Rna-Protein Interactions And Annotations, Kateland Sipe

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.