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ABSTRACT Genomes are biologically complex entities where an alteration in structure can yield no effect, or have a devastating effect on many pathways. Most of the focus has been on translocations that generate fusion proteins. However, this is only one of many outcomes. Recent work suggests alterations in topologically associated domains (TADs) can lead to changes in gene expression. It is hypothesized that alterations in genome structure can disrupt TADs leading to an alteration in the variability of gene expression within the contained gene expression neighborhood defined by the TAD. To test this hypothesis, variability of gene expression for genes contained within TADs between 37 cancer cell lines from the NCI-60 cell line panel was compared with normal expression data for the corresponding tissues of origin. Those results were correlated with the data on structural events within the NCI-60 cell lines that would disrupt a TAD. It was observed that 2.4% of the TADs displayed altered variance in gene expression when comparing cancer to normal tissue. Using array CGH data from the cancer cell lines to map breakpoints within TADS, it was discovered that altered variance is always associated with a TAD disrupted by a breakpoint, but a breakpoint within a TAD does not always lead to altered variance. TADs with altered variance in gene expression were no different in size than those without altered variance. There is evidence of recurrent pan-cancer alteration in variance for eleven genes within two TADs on two chromosomes (Chromosome 10 & 19) for all 37 cell lines. The genes located within these TADs are enriched in pathways related to RNA processing. This study supports altered variance as a signal of a breakpoint with a functional consequence.
ContributorsDunham, Jocelen Michaela (Author) / Kanthaswamy, Sreethan (Thesis advisor) / Mancenido, Michelle (Thesis advisor) / Bussey, Kimberly J. (Committee member) / Arizona State University (Publisher)
Created2022
Description
Artificial Intelligence (AI) systems have achieved outstanding performance and have been found to be better than humans at various tasks, such as sentiment analysis, and face recognition. However, the majority of these state-of-the-art AI systems use complex Deep Learning (DL) methods which present challenges for human experts to design and evaluate such models with respect to privacy, fairness, and robustness. Recent examination of DL models reveals that representations may include information that could lead to privacy violations, unfairness, and robustness issues. This results in AI systems that are potentially untrustworthy from a socio-technical standpoint. Trustworthiness in AI is defined by a set of model properties such as non-discriminatory bias, protection of users’ sensitive attributes, and lawful decision-making. The characteristics of trustworthy AI can be grouped into three categories: Reliability, Resiliency, and Responsibility. Past research has shown that the successful integration of an AI model depends on its trustworthiness. Thus it is crucial for organizations and researchers to build trustworthy AI systems to facilitate the seamless integration and adoption of intelligent technologies. The main issue with existing AI systems is that they are primarily trained to improve technical measures such as accuracy on a specific task but are not considerate of socio-technical measures. The aim of this dissertation is to propose methods for improving the trustworthiness of AI systems through representation learning. DL models’ representations contain information about a given input and can be used for tasks such as detecting fake news on social media or predicting the sentiment of a review. The findings of this dissertation significantly expand the scope of trustworthy AI research and establish a new paradigm for modifying data representations to balance between properties of trustworthy AI. Specifically, this research investigates multiple techniques such as reinforcement learning for understanding trustworthiness in users’ privacy, fairness, and robustness in classification tasks like cyberbullying detection and fake news detection. Since most social measures in trustworthy AI cannot be used to fine-tune or train an AI model directly, the main contribution of this dissertation lies in using reinforcement learning to alter an AI system’s behavior based on non-differentiable social measures.
ContributorsMosallanezhad, Ahmadreza (Author) / Liu, Huan (Thesis advisor) / Mancenido, Michelle (Thesis advisor) / Doupe, Adam (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2023