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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Cancer is a disease which can affect all animals across the tree of life. Certain species have undergone natural selection to reduce or prevent cancer. Mechanisms to block cancer may include, among others, a species possessing additional paralogues of tumor suppressor genes, or decreasing the number of oncogenes within their genome. To understand cancer prevention patterns across species, I developed a bioinformatic pipeline to identify copies of 545 known tumor suppressor genes and oncogenes across 63 species of mammals. I used phylogenetic regressions to test for associations between cancer gene copy numbers and a species’ life history. I found a …

Contributors
Schneider-Utaka, Aika Kunigunda, Maley, Carlo C, Wilson, Melissa A, et al.
Created Date
2019

The processes of a human somatic cell are very complex with various genetic mechanisms governing its fate. Such cells undergo various genetic mutations, which translate to the genetic aberrations that we see in cancer. There are more than 100 types of cancer, each having many more subtypes with aberrations being unique to each. In the past two decades, the widespread application of high-throughput genomic technologies, such as micro-arrays and next-generation sequencing, has led to the revelation of many such aberrations. Known types and subtypes can be readily identified using gene-expression profiling and more importantly, high-throughput genomic datasets have helped identify …

Contributors
Yellapantula, Venkata Divya Teja, Dinu, Valentin, Scotch, Matthew, et al.
Created Date
2014

The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of …

Contributors
Torres Garcia, Wandaliz, Meldrum, Deirdre R., Runger, George C., et al.
Created Date
2011