Bioinformatics Commons^™

The Effects Of Mapk Signaling On The Development Of Cerebellar Granule Cells, Kerry Morgan

Honors Scholar Theses

The granule cells are the most abundant neuronal type in the human brain. Rapid proliferation of granule cell progenitors results in dramatic expansion and folding of the cerebellar cortex during postnatal development. Mis-regulation of this proliferation process causes medulloblastoma, the most prevalent childhood brain tumor. In the developing cerebellum, granule cells are derived from Atoh1-expressing cells, which arise from the upper rhombic lip (the interface between the roof plate and neuroepithelium). In addition to granule cells, the Atoh1 lineage also gives rise to different types of neurons including cerebellar nuclei neurons. In the current study, I have investigated the …

Mucin And Splice Variant Profiles Of Pancreatic Adenocarcinoma Predict Patient Survival And Subtyping, Christopher M. Thompson

Theses & Dissertations

PDAC is a pancreatic epithelial malignancy and demonstrates aggressive progression and bleak patient prognosis. Despite decades of research, the evolution of novel diagnostics and intervention modalities for PDAC is stagnant. This dissertation explores the characteristic aberrant and elevated expression of mucins in PDAC. Beginning with the hypothesis that mucins are associated with disease aggressiveness, analysis of PDAC patient survival in TCGA revealed no associations between single mucin expression and patient survival. This led to the underlying issue of PDAC tumor cellularity since this disease demonstrates variability in the proportion of cancer cells within the tumor. Tumor purity assessed with the …

Development Of In-Silico Pipelines For Identification And Characterization Of Biomarker Panels And Therapeutic Interventions In Gastro-Intestinal (Gi) Cancers, Pranita Atri

Theses & Dissertations

Gastro-intestinal (GI) malignancies, including gastric, colorectal, and pancreatic cancers, have maintained their high overall mortality due to a lack of prognostic and diagnostic biomarkers and potential therapeutic modalities. While efforts have been made to improve both early detection and therapeutic interventions in these cancers, failure of conventional approaches have proven to be a big challenge, and alternate approaches are needed. Computational biology approaches owing to lesser time and more per target success rate offer a unique solution here. The current study explored the use of computational biology techniques to study the various aspects relating to GI malignancies. First, we sought …

Reach, Effectiveness, Adoption, And Maintenance Of Mobile Electronic Clinical Decision Support Tools Deployed As Part Of National Quality Improvement Projects, Ellen K. Kerns

Theses & Dissertations

Electronic clinical decision support (ECDS) tools are often developed within quality improvement (QI) projects to increase adherence with the latest clinical practice guidelines. However, the potential reach and maintenance of ECDS use beyond the time and location of their associated project are very limited. Deploying ECDS using a mobile app (mECDS) has shown the potential to be a viable method of overcoming these limitations. However, it is unclear what pattern the reach and adoption of such a tool might follow and what effect this use has on clinical practice. Our team developed an app which contained two different mECDS tools …

Applications Of Comparative Genomics And Data Science To Agricultural And Clinical Research, Katrina A. Schlum

Doctoral Dissertations

The advent of inexpensive, high-throughput whole genome sequencing (WGS) technologies has led to the generation of thousands of related genomes, even from a single study. Large-scale genome analysis has resulted in hypothesis-generating approaches in the fields of clinical, human and agriculture genomics. Additionally, population-level genomic sampling has resulted in a decrease in false positives in genotype-phenotype associations and an increase in understanding of the basis of disease, antibiotic and pesticide resistance. Deeper understanding of migration, genetic divergence and evolution has also been made possible due to WGS. This research applies comparative genomics, population genomics and data science approaches to whole …

Engineering Modularity Of Ester Biosynthesis Across Biological Scales, Hyeongmin Seo

Doctoral Dissertations

Metabolic engineering and synthetic biology enable controlled manipulation of whole-cell biocatalysts to produce valuable chemicals from renewable feedstocks in a rapid and efficient manner, helping reduce our reliance on the conventional petroleum-based chemical synthesis. However, strain engineering process is costly and time-consuming that developing economically competitive bioprocess at industrial scale is still challenging. To accelerate the strain engineering process, modular cell engineering has been proposed as an innovative approach that harnesses modularity of metabolism for designing microbial cell factories. It is important to understand biological modularity and to develop design principles for effective implementation of modular cell engineering. In this …

Biomedical Informatics Colloquium, Bio 4050, Course Outline, Eugenia G. Giannopoulou

Open Educational Resources

A seminar-based course that exposes students to current research topics in the fields of Bioinformatics and Medical Informatics. Weekly presentations by invited speakers and/or faculty introduce students to the broad diversity of research areas in both fields, and engages them in critical thinking and writing. Online lectures and reading activities will be given periodically.

Rare Variant Association Studies In Crohn’S Disease And Colorectal Cancer: Methods And Applications, Jiun-Sheng Chen

Dissertations & Theses (Open Access)

Genetic factors account for a substantial portion of Crohn’s disease and colorectal cancer (CRC) risk. Patients with Crohn’s disease, a condition that causes chronic inflammation of the gastrointestinal tract, are at increased risk of colorectal cancer morbidity and mortality. Genome-wide association studies using single marker approaches have identified loci responsible for these diseases, but disease susceptibility from rare variants is incompletely understood. This dissertation includes three chapters, two association studies for Crohn’s disease and CRC, and a statistical method to improve the power of statistical tests.

For Crohn’s disease, we performed targeted sequencing of 101 genes in 205 children with …

Gene Selection And Classification In High-Throughput Biological Data With Integrated Machine Learning Algorithms And Bioinformatics Approaches, Abhijeet R Patil

Open Access Theses & Dissertations

With the rise of high throughput technologies in biomedical research, large volumes of expression profiling, methylation profiling, and RNA-sequencing data are being generated. These high-dimensional data have large number of features with small number of samples, a characteristic called the "curse of dimensionality." The selection of optimal features, which largely affects the performance of classification algorithms in machine learning models, has led to challenging problems in bioinformatics analyses of such high-dimensional datasets. In this work, I focus on the design of two-stage frameworks of feature selection and classification and their applications in multiple sets of colorectal cancer data. The first …

Trunctrimmer: A First Step Towards Automating Standard Bioinformatic Analysis, Z. Gunner Lawless, Dana Dittoe, Dale R. Thompson, Steven C. Ricke

Computer Science and Computer Engineering Undergraduate Honors Theses

Bioinformatic analysis is a time-consuming process for labs performing research on various microbiomes. Researchers use tools like Qiime2 to help standardize the bioinformatic analysis methods, but even large, extensible platforms like Qiime2 have drawbacks due to the attention required by researchers. In this project, we propose to automate additional standard lab bioinformatic procedures by eliminating the existing manual process of determining the trim and truncate locations for paired end 2 sequences. We introduce a new Qiime2 plugin called TruncTrimmer to automate the process that usually requires the researcher to make a decision on where to trim and truncate manually after …

Simulation Of The Interaction Between Striated Muscle Unc-45 And Transcription Factor Gata-4, Drake Alexander Duncan

Electronic Theses and Dissertations

Striated Muscle UNC-45, also known as UNC-45b, is an important protein that acts as a chaperone for myosin in cardiac and skeletal muscles, binding to myosin at its C-terminal UCS domain and regulating its assembly into thick filaments and sarcomeric structures. The UCS domain contains a large loop that is believed to be the first point of interaction between myosin and UNC-45b. GATA-4 is an essential transcription factor that facilitates transcription of several genes in cardiac development, particularly alpha-heavy chain myosin in heart tissue. Recently, studies have shown that there is interaction of GATA-4 with UNC-45b and that GATA-4 binds …

Discovery Of Novel Ubiquitin- And Methylation-Dependent Interactions Using Protein Domain Microarrays, Jianji Chen

Dissertations & Theses (Open Access)

Post-translational modifications (PTMs) drive signal transduction by interacting with "reader" proteins. Protein domain microarray is a high throughput platform to identify novel readers for PTMs. In this dissertation, I applied two protein domain microarrays identifying novel readers for histone H2Aub1 and H2Bub1, and H3TM K4me3. Ubiquitinations of histone H2A at K119 (H2Aub1) and histone H2B at K120 (H2Bub1) function in distinct transcription regulation and DNA damage repair pathways, likely mediated by specific "reader" proteins. There are only two H2Aub1-specific readers identified and no known H2Bub1-specific readers. Using a ubiquitin-binding domain microarray, I discovered the phospholipase A2-activating protein (PLAA) PFU domain …

Computational Approaches To Delineate Transcriptional And Functional Heterogeneity In Pancreatic Cancer, Sanjana Srinivasan

Dissertations & Theses (Open Access)

Pancreatic ductal adenocarcinoma (PDAC) is an incurable disease characterized by poor survival, dense desmoplastic stroma and activating mutations in KRAS (>90%). These tumors are highly complex ecosystems composed of molecularly distinct sub-populations that exhibit a spectrum of genetic features and associated phenotypes. Despite recent advances in the transcriptomic characterization of PDAC into at least two tumor subtypes, this alone has been insufficient to define more specific patterns of oncogenic dependency. To fully leverage advancements in next generation sequencing and functional genomics, we have sought to establish computational methodologies to aid in refined target discovery, and to develop a novel …

Biases And Blind-Spots In Genome-Wide Crispr-Cas9 Knockout Screens, Merve Dede

Dissertations & Theses (Open Access)

Adaptation of the bacterial CRISPR-Cas9 system to mammalian cells revolutionized the field of functional genomics, enabling genome-scale genetic perturbations to study essential genes, whose loss of function results in a severe fitness defect. There are two types of essential genes in a cell. Core essential genes are absolutely required for growth and proliferation in every cell type. On the other hand, context-dependent essential genes become essential in an environmental or genetic context. The concept of context-dependent gene essentiality is particularly important in cancer, since killing cancer cells selectively without harming surrounding healthy tissue remains a major challenge. The toxicity of …

Predicting Factors Of Re-Hospitalization After Medically Managed Intensive Inpatient Services In Opioid Use Disorder, Brian Kay

Theses and Dissertations

IntroductionOpioid use disorder has continued to rise in prevalence across the United States, with an estimated 2.5 million Americans ailing from the condition (NIDA, 2020). Medically managed detoxification incurs substantial costs and, when used independently, may not be effective in preventing relapse (Kosten & Baxter, 2019). While numerous studies have focused on predicting the factors of developing opioid use disorder, few have identified predictors of readmission to medically managed withdrawal at an inpatient level of care. Utilizing a high-fidelity dataset from a large multi-site behavioral health hospital, these predictors are explored.

MethodsPatients diagnosed with Opioid Use Disorder and hospitalized in …

Machine Learning Models For Deciphering Regulatory Mechanisms And Morphological Variations In Cancer, Saman Farahmand

Graduate Doctoral Dissertations

The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances …

Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr.

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation, I aim to forecast high utilizers of emergency care and inpatient Medicare services (i.e., healthcare visits). Through a literature review, I demonstrate that accurate and reliable prediction of these future high utilizers will not only reduce healthcare costs but will also improve the overall quality of healthcare for patients. By identifying this population at risk before manifestation, I propose that there is still time to reverse undesirable healthcare trajectories (i.e., individuals whose clinical risk increases an excessive healthcare and treatment burden) through timely attention and proper care coordination. My dissertation culminates in the delivery of state-of-the-art predictive …

Book Review: Foundations Of Artificial Intelligence In Healthcare And Bioscience, Paul B. Freeman Od

Optometric Clinical Practice

Book Review:

Catania LJ. Foundations of Artificial Intelligence in Healthcare and Bioscience. Cambridge: Academic Press; 2020, $150.00, 524 pages, Paperback ISBN:9780128244777, e-book ISBN: 9780323860055.

Unsupervised And Supervised Learning For Rna-Protein Interactions And Annotations, Kateland Sipe

Honors Projects

This project analyzed the base and amino acid interactions and annotations through the use of unsupervised and supervised learning techniques. For unsupervised learning, clustering found the data was not able to be distinguished into clear groups which matched the original annotations through kmeans clustering and hierarchical clustering. For supervised learning, the use of random forest, glmnet, and deep learning neural networks were successful in creating accurate predictions. However, machine learning likely will not be able to replace the original complex program, but could be used for possible simplification.

Visualization And Interpretation Of Protein Interactions, Dipanjan Chatterjee

Electronic Thesis and Dissertation Repository

Visualization and interpretation of deep learning models' prediction is a very important area of research in machine learning nowadays. Researchers are not only focused on generating a model with good performance, but also they want to trust the model. Our aim in this thesis is to adapt existing interpretation methods to a protein-protein binding site prediction problem to visualize and understand the model's prediction and learning pattern.

We present three deep learning-based interpretation methods: sensitivity analysis, saliency map and integrated gradients to analyze the amino acid residues which create positive and negative relevance to the deep learning models' prediction. As …