dc.contributor.advisor | Cheng, Jianlin | eng |
dc.contributor.author | Highsmith, Max Richard | eng |
dc.date.issued | 2021 | eng |
dc.date.submitted | 2021 Fall | eng |
dc.description.abstract | This dissertation, submitted as a partial requirement for completion of the Doctorate of Philosophy, outlines the research performed by Max Highsmith in the BDM Lab. This work includes a functional expansion of a three-dimensional genome conformation database, the development of a novel, deep-learning based strategy for the enhancement of Hi-C data, The development of deep learning approach for domain identification using epigenetic features, and the development of a novel computational tool for 4D modeling of chromosome dynamics. | eng |
dc.description.bibref | Includes bibliographical references. | eng |
dc.format.extent | xiii, 110 pages : illustrations (color) | eng |
dc.identifier.uri | https://hdl.handle.net/10355/93230 | |
dc.language | English | eng |
dc.publisher | University of Missouri--Columbia | eng |
dc.title | Structural modeling of the 3D genome using machine learning | eng |
dc.type | Thesis | eng |
thesis.degree.discipline | Computer science (MU) | eng |
thesis.degree.level | Doctoral | eng |
thesis.degree.name | Ph. D. | eng |