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Applying Unsupervised Multi-Omic Learning To Identify Patterns Of Human Genomic Regulatory Regions With An Emphasis In Characterizing Hervh Loci., Corinne Sexton

UNLV Theses, Dissertations, Professional Papers, and Capstones

With the increase of diverse genomic data types, machine learning provides an opportunity to integrate several omics datasets into one cohesive annotation. In this dissertation, I apply an unsupervised clustering approach to a novel representation of 3D chromosome conformation data and chromatin mark data. Specifically I use this new method to annotate the regulatory function of human endogenous retrovirus H (HERVH). In chapter 1, I propose a synthesized model of HERVH function as an activating lncRNA based on previously published work. As HERVH and transposable elements in general are repetitive due to their methods of retrotransposition, in chapter 2 I …