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MUIDSI DISSERTATION DEFENSE: Explainable Artificial Intelligence To Stratify Pan-Cancer Patients For Immune Checkpoint Inhibitor Decision Making
Immune checkpoints are a normal part of the immune system. It engages when proteins on the surface of immune cells called T cells recognize and bind to partner proteins on other cells, such as some tumor cells. Immune based therapies such as ICIs work by blocking checkpoint proteins from binding with their partner proteins. This
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Dissertation Defense: DESIGN AND DEVELOPMENT OF GEOSPATIAL ANALYTICAL RESEARCH KNOWLEDGEBASE (GeoARK)
A consistent finding across health, social, business, and environmental literature is that location matters. To conduct impactful research that can be applied to real-world issues and problems, the research must be grounded within the context of the real world in both place and culture. Significant differences exist and can vary across scales from blocks to
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Dissertation Defense – UNDERSTANDING GENOME COMPOSITION OF EUSOCIAL HYMENOPTERAN INSECTS
Genome sequencing of the Western honey bee (Apis mellifera), a model for the biology and evolution of eusocial behavior, has revealed unusual genome compositional characteristics, including a low but heterogeneous GC content, bimodal GC content distribution, and a biased tendency of genes to be located in low GC regions. In this dissertation, we sought to
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An Evaluation of an Electronic Medical Record (EMR) Based System to Characterize and Correlate Physician Burnout and EMR Use
Burnout disproportionately affects healthcare workers and continues to rise, contributing to cost, quality, and patient safety risk in an already overburdened United States healthcare system. While the causes of burnout are complex, evidence suggests that Electronic Medical Record use (EMR) is one major contributor due to the increased clerical burden that decreases patient contact time and disrupts the provider clinical workflow. The challenge of improving the
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EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR PATIENT STRATIFICATION AND DRUG REPOSITIONING
Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning
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