MUIDSI DISSERTATION DEFENSE – ACCELERATING DATA-DRIVEN DISCOVERY IN TYPE 1 DIABETES: AN INFORMATICS-BASED APPROACH

Type 1 diabetes (T1D) is a lifelong chronic disease characterized by the absolute or near-absolute loss of insulin. For affected individuals, management of T1D is an unremitting challenge that involves constant blood glucose monitoring and lifelong administration and titration of exogeneous insulin. Unfortunately, findings from decades of research have not yet comprehensively translated into substantially improved health outcomes, suggesting that limitations inherent in the use of small patient samples and traditional analytical methods have curbed discovery of actionable disease insights. Understanding and addressing ongoing worsened health outcomes in T1D – as well as particular vulnerabilities experienced by subgroups of individuals…

MUIDSI Dissertation Defense: IDENTIFICATION OF IMMUNE-RELATED GENE SIGNATURES TO EVALUATE IMMUNOTHARAPEUTIC RESPONSE IN CANCER PATIENTS USING EXPLORATORY SUBGROUP DISCOVERY

Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients’ heterogeneity, immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs will not respond well, which motivates the exploration of immunotherapy in combination with either targeted treatments or chemotherapy. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although, matching patient subgroup to treatment option that can improve patients’ health outcome remains a challenging task.  We extend our exploratory subgroup discovery algorithm to identify patient subpopulations…

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 prevents the “off” signal from being sent, allowing the T cells to kill cancer cells. One such drug act against a checkpoint protein called PD-1 or its partner protein PD-L1. Some tumors turn down the T cell response by producing lots of PD-L1. Recent years…

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 neighborhoods to regions. The collection, integration, and use of varied data are foundational to addressing the complex questions of today’s health research. To strategically transform this research, a robust integrated data platform is needed. This research presentation focuses on the design, development, implementation, and use…

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 determine whether those features are specific to Apis or shared with other insects and the biological meaning of those features. Chapter 1 reviews the major concepts that tie my dissertation research together, highlighting the importance of recombination, GC composition, and their relationship to the evolution of eusociality.

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 physician EMR experience is exacerbated both by variability across venues and specialty.  Targeted training and optimization efforts are generally deployed one-time at a clinic or specialty level but are challenging to deploy longitudinally and in surveillance mode due to the cost and effort of administering traditional survey instruments. To address this…

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 is an essential alternative for developing new drugs for a disease subpopulation. There is a crucial need to develop data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI…