Bioinformatics Commons^™

Comprehensive Characterization Of Covid-19 Patients With Repeatedly Positive Sars-Cov-2 Tests Using A Large U.S. Electronic Health Record Database., Xiao Dong, Yujia Zhou, Xiao-Ou Shu, Elmer V Bernstam, Rebecca Stern, David M Aronoff, Hua Xu, Loren Lipworth

Journal Articles

In the absence of genome sequencing, two positive molecular tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible reinfection. Using a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results ≥60 days apart, separated by ≥2 consecutive negative test results. The prevalence of chronic medical conditions, symptoms, and severe outcomes related to coronavirus disease 19 (COVID-19) illness were ascertained. The median age of patients was 64.5 years, 40% were Black, and 39% …

Unveiling Global Roles Of G-Quadruplexes And G4-22 In Human Genetics, Ruth Barros De Paula

Dissertations & Theses (Open Access)

G-quadruplexes are non-B DNA structures formed by four or more runs of repeated guanines that confer unique features to living organism’s genomes. These sequences are enriched in regulatory regions, such as promoters and 5’ UTRs, and have distinct regulatory roles in both health and disease states. Even though previous studies showed the impact of G4 in gene expression, none of them summarized the location-specific effect of G4. Also, there is no broad understanding about the most common G4 repeat in the human genome, named here as G4-22, and how it links to the evolution of mammals and their biology. In …

Impact Of Intratumor Heterogeneity And The Tumor Microenvironment In Shaping Tumor Evolution And Response To Therapy, Akash Mitra

Dissertations & Theses (Open Access)

Intratumor heterogeneity (ITH) is a crucial challenge in cancer treatment. The genotypic and phenotypic heterogeneity underlying diverse cancer types leads to subclonal variation, which may result in mixed or failed response to therapy. The heterogeneity at the tumor level, along with the tumor microenvironment (TME), often shapes tumor evolution and ultimately clinical outcome. Given that modern treatment paradigms increasingly expose patients with metastatic disease to multiple treatment modalities through the course of their disease, there exists a need to characterize robust and predictive biomarkers of response to therapy. In order to accurately characterize tumor evolution, we need to account for …

Digital Technology Needs In Maternal Mental Health: A Qualitative Inquiry., Alexandra Zingg, Laura Carter, Deevakar Rogith, Amy Franklin, Sudhakar Selvaraj, Jerrie Refuerzo, Sahiti Myneni

Journal Articles

Digital technologies offer many opportunities to improve mental healthcare management for women seeking pre- and-postnatal care. They provide a discrete, practical medium that is well-suited for the sensitive nature of mental health. Women who are more prone to experiencing peripartum depression (PPD), such as those of low-socioeconomic background or in high-risk pregnancies, can benefit the most from such technologies. However, current digital interventions directed towards this population provide suboptimal support, and their responsiveness to end user needs is quite limited. Our objective is to understand the digital terrain of information needs for low-socioeconomic status women with high-risk pregnancies, specifically within …

Med-Bert: Pretrained Contextualized Embeddings On Large-Scale Structured Electronic Health Records For Disease Prediction., Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, Degui Zhi

Journal Articles

Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pretraining of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by BERT, we propose Med-BERT, which adapts the BERT framework originally developed …

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 …

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 …

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 …

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 …

Generalized And Transferable Patient Language Representation For Phenotyping With Limited Data., Yuqi Si, Elmer V Bernstam, Kirk Roberts

Journal Articles

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 …

Exchanges In A Virtual Environment For Diabetes Self-Management Education And Support: Social Network Analysis., Carlos A Pérez-Aldana, Allison A Lewinski, Constance M Johnson, Allison A Vorderstrasse, Sahiti Myneni

Journal Articles

BACKGROUND: Diabetes remains a major health problem in the United States, affecting an estimated 10.5% of the population. Diabetes self-management interventions improve diabetes knowledge, self-management behaviors, and clinical outcomes. Widespread internet connectivity facilitates the use of eHealth interventions, which positively impacts knowledge, social support, and clinical and behavioral outcomes. In particular, diabetes interventions based on virtual environments have the potential to improve diabetes self-efficacy and support, while being highly feasible and usable. However, little is known about the patterns of social interactions and support taking place within type 2 diabetes-specific virtual communities.

OBJECTIVE: The objective of this study was to …