Data Science Commons^™

A Machine Learning Approach To Discovering Physical Models Of Galaxy Formation, Festa Bucinca

Dissertations, Theses, and Capstone Projects

Galaxies are the breathtakingly beautiful starry islands of the Universe. The process of galaxy formation involves the transformation from simple initial conditions in the early Universe to the complex galaxy structures we observe today. Spanning an immense spatial range and tremendous time scales - from the vastness of the Universe to the scale of individual stars - the physics of galaxy formation is both complex and crucial for understanding the Universe we live in. However, despite significant advancements, our theoretical understanding of galaxy formation remains incomplete.

In the era of big data available from hydrodynamical simulations and observations, Machine Learning …

Midwest Data Librarian Symposium: A Model For Regional Communities, Amy Koshoffer, Kelsey Badger, Kristen Adams, Ana Munandar

Library Articles and Research

The Midwest Data Librarian Symposium (MDLS) is an annual unconference covering data and data librarianship. The symposium aims to provide a venue for librarians and others interested in the topics to network, discuss issues related to research data management, and learn from each other. While most of the attendees are from the Midwest area, the symposium is open to all.

MDLS is a community-led effort and has no governing body. In this article, we share our experiences in contributing to MDLS, including the recent MDLS 2023. Coming up on its tenth year in 2024, we believe MDLS offers a valuable …

Data Driven Decision Making For Sustainable Planning And Operations Of Large Scale Networks, Bahareh Kargar

Dissertations

This dissertation explores data-driven decision-making networks, focusing on sustainable planning and operations for large-scale systems such as healthcare supply chains and power systems. One significant application in healthcare is the optimization of vaccine supply chains. An agent-based simulation-optimization modeling framework is developed to enhance the efficiency and sustainability of vaccine distribution. First, an agent-based epidemiological model of COVID-19 is extended to capture disease transmission dynamics and forecast the number of susceptible individuals and infections. Then, a sustainable vaccine supply chain considering the impacts of greenhouse gases is developed and integrated with the simulation model to minimize total costs and environmental …

A Methodological Framework For Ontology Development, Enrichment, And Application In Natural Language Processing Tasks, Navya Martin Kollapally

Dissertations

Electronic Health Records (EHRs) have been widely used in healthcare to record demographics, vital signs, test results, immunizations, medical imaging reports, differential diagnoses, etc. It is now accepted that non-clinical (e.g., social) factors have a substantial influence on health outcomes. Hence, it is desirable to record these Social and Commercial Determinants of Health (SDoH & CDoH) in an EHR. The "non-text parts" of EHR notes (e.g., data tables) rely on coded terms from underlying ontologies or terminologies to facilitate semantic interoperability. Ontologies help define concepts, the relationships between them, and instances that can be utilized in research.

The first accomplishment …

Special Supplement Issue On Quality Assurance And Enrichment Of Biological And Biomedical Ontologies And Terminologies, Licong Cui, Ankur Agrawal

Faculty, Staff and Student Publications

Ontologies and terminologies serve as the backbone of knowledge representation in biomedical domains, facilitating data integration, interoperability, and semantic understanding across diverse applications. However, the quality assurance and enrichment of these resources remain an ongoing challenge due to the dynamic nature of biomedical knowledge. In this editorial, we provide an introductory summary of seven articles included in this special supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. These articles span a spectrum of topics, such as development of automated quality assessment frameworks for Resource Description Framework (RDF) resources, identification of missing concepts in SNOMED …

Perceptions Of Hiv-Related Comorbidities And Usability Of A Virtual Environment For Cardiovascular Disease Prevention Education In Sexual Minority Men With Hiv: Formative Phases Of A Pilot Randomized Controlled Trial, S Raquel Ramos, Harmony Reynolds, Constance Johnson, Gail Melkus, Trace Kershaw, Julian F Thayer, Allison Vorderstrasse

Faculty, Staff and Student Publications

Background: Sexual minority men with HIV are at an increased risk of cardiovascular disease (CVD) and have been underrepresented in behavioral research and clinical trials.

Objective: This study aims to explore perceptions of HIV-related comorbidities and assess the interest in and usability of a virtual environment for CVD prevention education in Black and Latinx sexual minority men with HIV.

Methods: This is a 3-phase pilot behavioral randomized controlled trial. We report on formative phases 1 and 2 that informed virtual environment content and features using qualitative interviews, usability testing, and beta testing with a total of 25 individuals. In phase …

Sexannodb, A Knowledgebase Of Sex-Specific Regulations From Multi-Omics Data Of Human Cancers, Mengyuan Yang, Yuzhou Feng, Jiajia Liu, Hong Wang, Sijia Wu, Weiling Zhao, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

Background

Sexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules.

Methods

In this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, …

Analysis Of Serum Exosome Metabolites Identifies Potential Biomarkers For Human Hepatocellular Carcinoma, Tingting Zhao, Yan Liang, Xiaolan Zhen, Hong Wang, Li Song, Didi Xing, Hui Li

Faculty, Staff and Student Publications

Currently, the clinical cure rate for primary liver cancer remains low. Effective screening and early diagnosis of hepatocellular carcinoma (HCC) remain clinical challenges. Exosomes are intimately associated with tumor development and their contents have the potential to serve as highly sensitive tumor-specific markers. A comprehensive untargeted metabolomics study was conducted using exosome samples extracted from the serum of 48 subjects (36 HCC patients and 12 healthy controls) via a commercial kit. An ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) strategy was used to identify the metabolic compounds. A total of 18 differential metabolites were identified using the non-targeted metabolomics approach of UPLC-QTOF-MS/MS. …

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, And Artificial Intelligence To Interrogate The Metabolic Evolution Of Glioblastoma, Kang Lin Hsieh, Qing Chen, Travis C Salzillo, Jian Zhang, Xiaoqian Jiang, Pratip K Bhattacharya, Shyan Shams

Faculty, Staff and Student Publications

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs …

Mathematical Modeling, Analysis, And Simulation Of Patient Addiction Journey, Adan Baca, Diego Gonzalez, Alonso G. Ogueda, Holly C. Matto, Padmanabhan Seshaiyer

CODEE Journal

This paper aims to develop a mathematical model to study the dynamics of addiction as individuals go through their detox journey. The motivation for this work is three fold. First, there has been a significant increase in drug overdose and drug addiction following the COVID-19 pandemic, and addiction may be interpreted as a infectious disease. Secondly, the dynamics of infectious disease could be modeled via compartmental models described by differential equations and one can therefore leverage the existing analytical and numerical methods to model addiction as a disease. Finally, the work helps to inform how mathematical models governed by differential …

Cluster Effect For Snp-Snp Interaction Pairs For Predicting Complex Traits, Hui Yi Lin, Harun Mazumder, Indrani Sarkar, Po Yu Huang, Rosalind A. Eeles, Zsofia Kote-Jarai, Kenneth R. Muir, Johanna Schleutker, Nora Pashayan, Jyotsna Batra, David E. Neal, Sune F. Nielsen, Børge G. Nordestgaard, Henrik Grönberg, Fredrik Wiklund, Robert J. Macinnis, Christopher A. Haiman, Ruth C. Travis, Janet L. Stanford, Adam S. Kibel, Cezary Cybulski, Kay Tee Khaw, Christiane Maier, Stephen N. Thibodeau, Manuel R. Teixeira, Lisa Cannon-Albright, Hermann Brenner, Radka Kaneva, Hardev Pandha, Et Al

School of Public Health Faculty Publications

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP-SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP-SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP-SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a …

Disparities And Protective Factors In Pandemic-Related Mental Health Outcomes: A Louisiana-Based Study, Ariane L. Rung, Evrim Oral, Tyler Prusisz, Edward S. Peters

School of Public Health Faculty Publications

Introduction: The COVID-19 pandemic has had a wide-ranging impact on mental health. Diverse populations experienced the pandemic differently, highlighting pre-existing inequalities and creating new challenges in recovery. Understanding the effects across diverse populations and identifying protective factors is crucial for guiding future pandemic preparedness. The objectives of this study were to (1) describe the specific COVID-19-related impacts associated with general well-being, (2) identify protective factors associated with better mental health outcomes, and (3) assess racial disparities in pandemic impact and protective factors. Methods: A cross-sectional survey of Louisiana residents was conducted in summer 2020, yielding a sample of 986 Black …

Charlotte: A Modern Tool For Cave Surveying, Luca Tringali Mr., Giacomo Canciani Dr., Alexander Debenjak, Tecla Tripari

International Journal of Speleology

The future of cave surveying is a full 3D scan, as automatic as possible, capturing all morphologically relevant details. However, the vast majority of cavers are still using tools designed more than 15 years ago. The advancements in tools for architects, CAD designers, programmers of VR and robotics could be helpful also for the caving community. That’s why Charlotte, a cheap DIY 2.5D scanner designed for cave surveying, was developed and built. To demonstrate its capabilities, a new survey of Grotta Regina del Carso cave (cad. nr. 2328/4760VG), the biggest cave in the Gorizia Karst, has been realized and published. …

Knowledge Management And Semantic Reasoning: Ontology And Information Theory Enable The Construction Of Knowledge Bases And Knowledge Graphs, Quynh D. Tran, Ozan Dernek, Erika I. Barcelos, Laura S. Bruckman, Roger H. French

Researchers, Instructors, & Staff Scholarship

FAIR (Findable, Accessible, Interoperable, Reusable) principles are guidelines Wilkinson, et. al. (2016) proposed for data governance and stewardship. Ontology is a powerful tool that can achieve many aspects of all four FAIR principles. Unfortunately, there is a misconception about ontology that it is only useful for establishing FAIR data. We need to think beyond data to answer the question “So what?” after an ontology is developed. It is critical to apply FAIR principles to results, analysis, and models, which is where the concept of digital thread comes in. FAIRified results, analysis, and models can be stored in a knowledge base …

Knowledge Management Of Historical Data: Ontology Development For Chemical Reactions, Quynh D. Tran, Alexander Harding Bradley, Balashanmuga Priyan Rajamohan, Jonathan E. Gordon, Van D. Tran, Kiefer Lin, Erika I. Barcelos, Laura S. Bruckman, Roger H. French

Researchers, Instructors, & Staff Scholarship

Knowledge management of the literature and historical data is critical to accelerated drug and materials discovery. Currently, literature knowledge is scattered in journal articles in various formats: diagrams, texts, plots, etc. Historical data from past experiments are saved in a number of local computers under confusing folder structures with ambiguous file names. To manage and organize historical data and knowledge, our group (SDLE) at CWRU follows FAIR (Findable, Accessible, Interoperable, Reusable) principles, which outline the best practices for data stewardship and data provenance, and ontology, a formal representation of terms and concepts and their relationships, as a tool to improve …

Uncertainty Quantification In Machine Learning Models Via Gaussian Process Regression: A Comparative Study, Ayorinde E. Olatunde, Weiqi Yue, Pawan K. Tripathi, Roger H. French, Anirban Mondal

Faculty Scholarship

As the use of Machine learning models in science and engineering continues to increase, there is an increasing need for quantifying the uncertainties inherent in the predictions of these models. The more complex a model is, the more the uncertainties in its predictions increase. Amongst the plethora of methodologies used in quantifying uncertainties lies Gaussian Process Regression (GPR). GPR surmounts some of the popular shortfalls of other state-of-the-art methodologies. Although GPR has some quick wins in its application for uncertainty quantification, it is plagued with some shortfalls, such as scalability issues when the feature space increases as well as an …

Enhancing Fundraising Strategies In Higher Education Through Machine Learning, Laith Alatwah

Electrical Engineering Theses

This thesis presents a comprehensive application of machine learning techniques, namely Fine Gaussian SVM and RUS Boosted Trees, to enhance fundraising strategies in higher education institutions. Analyzing a rich dataset from Blackbaud Raiser's Edge NXT, spanning 2012 to 2022, the study focuses on donor profiles, including demographics, donation history, and engagement patterns. Key demographic insights include the increasing engagement of younger donors (20-29 age group) and significant contributions from older donors (70-99 age group). Geographical trends are also examined, revealing distinct patterns based on donors' city, state, and ZIP code. The Fine Gaussian SVM model demonstrates moderate discriminatory power, with …

Mapping Urban Tree Canopy Using Publicly Available Satellite Data, Rosemary Mcguinness

Theses and Dissertations

This project addresses the need for accessible, cost-effective tools for quantifying spatial and temporal changes in tree canopy cover in urban areas. Urban tree canopy provides a wide range of ecosystem services, including lowering air temperatures, reducing pollution, and mitigating stormwater runoff. Cities around the world have placed the expansion of their urban forests at the center of their sustainability goals. Consistent and timely data on urban tree canopy is essential for urban greening initiatives to succeed. Existing methods of accessing information about urban tree canopy are highly technical, costly, and labor-intensive, while the freely available source of tree canopy …

Medical Image Analysis Based On Graph Machine Learning And Variational Methods, Sina Mohammadi

Computational and Data Sciences (PhD) Dissertations

This study explores advanced methodologies for enhancing brain tumor segmentation, addressing the complexity and diversity of tumor sub-regions in medical imaging. We introduce a novel approach utilizing Graph Neural Networks (GNNs) that incorporate both spectral and spatial insights for segmentation. By leveraging various supervoxel creation methods such as VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb-Huttenlocher, we structured 3D MRI images into a graph format. This format enabled the implementation of Spectral and Spatial GNNs to capture comprehensive local and global tumor characteristics effectively. Our Spectral-Spatial GNN model, integrating the Laplacian matrix, demonstrated significant improvements in segmenting distinct tumor sub-regions of Necrosis, …

Quantinar: A Blockchain Peer-To-Peer Ecosystem For Modern Data Analytics, Raul Bag, Bruno Spilak, Julian Winkel, Wolfgang Karl Hardle

Sim Kee Boon Institute for Financial Economics

The power of data and correct statistical analysis has never been more prevalent. Academics and practitioners require nowadays an accurate application of quantitative methods. Yet many branches are subject to a crisis of integrity, which is shown in an improper use of statistical models, p-hacking, HARKing, or failure to replicate results. We propose the use of a Peer-to-Peer (P2P) ecosystem based on a blockchain network, Quantinar, to support quantitative analytics knowledge paired with code in the form of Quantlets or software snippets. The integration of blockchain technology allows Quantinar to ensure fully transparent and reproducible scientific research.

Parameter Estimation For Stroke Patients Using Brain Ct Perfusion Imaging With Deep Temporal Convolutional Neural Network, Shake Ibna Abir

Masters Theses & Specialist Projects

Acute ischemic stroke, caused by cerebral artery blockage, is a leading cause of long-term disability and mortality. Effective management relies on accurate, timely assessments from neuroimaging data. Computed tomography perfusion (CTP) imaging is crucial in evaluating stroke patients, offering detailed maps of cerebral perfusion to identify irreversibly damaged tissue and at-risk areas. This detailed assessment is essential for informed therapeutic decisions.

Key perfusion parameters derived from CTP imaging, including cerebral blood volume (CBV), cerebral blood flow (CBF), time to peak (TTP), and mean transit time (MTT), are crucial for understanding the extent and nature of cerebral ischemia, providing valuable insights …

Forecasting Commercial Vehicle Miles Traveled (Vmt) In Urban California Areas, Steve Chung, Jaymin Kwon, Yushin Ahn

Mineta Transportation Institute

This study investigates commercial truck vehicle miles traveled (VMT) across six diverse California counties from 2000 to 2020. The counties—Imperial, Los Angeles, Riverside, San Bernardino, San Diego, and San Francisco—represent a broad spectrum of California’s demographics, economies, and landscapes. Using a rich dataset spanning demographics, economics, and pollution variables, we aim to understand the factors influencing commercial VMT. We first visually represent the geographic distribution of the counties, highlighting their unique characteristics. Linear regression models, particularly the least absolute shrinkage and selection operator (LASSO) and elastic net regressions are employed to identify key predictors of total commercial VMT. LASSO regression …

Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny

All Theses

High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …

Optimization Strategies To Enhance Performance In Matrix/Tensor Factorization And Multi-Source Data Integration, Mengyuan Zhang

All Dissertations

Optimization in the realm of machine learning constitutes a fundamental process aimed at refining the parameters of models to enhance their performance. It serves as the backbone of various machine learning techniques, encompassing diverse algorithms and methodologies tailored to address specific tasks and objectives.

In machine learning, datasets are commonly structured as matrices or tensors, making techniques like matrix factorization and tensor factorization indispensable for extracting meaningful representations from intricate data. Furthermore, datasets commonly comprise multiple sets of features, which has inspired our exploration of effective strategies for leveraging information from diverse sources during optimization. Additionally, the interconnected nature of …

Materials Data Science Ontology (Mds-Onto): Unifying Domain Knowledge In Materials And Applied Data Science, Van D. Tran, Jonathan E. Gordon, Alexander Harding Bradley, Balashanmuga Priyan Rajamohan, Quynh D. Tran, Gabriel Ponón, Yinghui Wu, Laura S. Bruckman, Erika I. Barcelos, Roger H. French

Student Scholarship

Ontologies have gained popularity in the scientific community as a means of standardizing concepts and terminology used in metadata across different institutions to facilitate data comprehension, sharing, and reuse. Despite the existence of frameworks and guidelines for building ontologies, the processes and standards used to develop ontologies still differ significantly, particularly in Materials Science. Our goal with the MDS-Onto Framework is to provide a unified and automated system for ontology development in the Materials and Data Sciences. This framework offers recommendations on where to publish ontologies online, how to best integrate them within the semantic web, and which formats to …

Patient-Centered Clinical Decision Support Challenges And Opportunities Identified From Workflow Execution Models, Dean F Sittig, Aziz Boxwala, Adam Wright, Courtney Zott, Nicole A Gauthreaux, James Swiger, Edwin A Lomotan, Prashila Dullabh

Faculty, Staff and Student Publications

OBJECTIVE: To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.

METHODS: To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee.

RESULTS AND DISCUSSION: We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new …

Automatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Modelsautomatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Models, Yang Ren, Yuqi Wu, Jungwei W Fan, Aditya Khurana, Sunyang Fu, Dezhi Wu, Hongfang Liu, Ming Huang

Faculty, Staff and Student Publications

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care.

MATERIALS AND METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural …

Artificial Intelligence In Fusion Protein Three-Dimensional Structure Prediction: Review And Perspective, Himansu Kumar, Pora Kim

Faculty, Staff and Student Publications

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action …

High Fat Diet & Social Isolation: Interactive Effects On Pain, Cognition, & Neuroinflammation, Ian M. Campuzano

Research Psychology Theses

Prior research has established a role for both social isolation and exposure to high fat Western diets in altering a range of behaviors from reduced memory performance to increased depression-like behaviors. The present study scrutinizes the interplay among these variables during the peri-adolescent developmental phase, utilizing Long-Evans rats as the experimental model. Our overarching hypothesis is that rats exposed to either social isolation, a high-fat diet, or both will result in heightened pain sensitivity, diminished cognitive flexibility, and increased neuroinflammatory responses within brain regions implicated in sociability, cognition, memory, and pain processing. Behavioral flexibility will be assessed using a maze-based …

Exploring The Diagnostic Potential Of Radiomics-Based Pet Image Analysis For T-Stage Tumor Diagnosis, Victor Aderanti

Electronic Theses and Dissertations

Cancer is a leading cause of death globally, and early detection is crucial for better

outcomes. This research aims to improve Region Of Interest (ROI) segmentation

and feature extraction in medical image analysis using Radiomics techniques

with 3D Slicer, Pyradiomics, and Python. Dimension reduction methods, including

PCA, K-means, t-SNE, ISOMAP, and Hierarchical Clustering, were applied to highdimensional features to enhance interpretability and efficiency. The study assessed the ability of the reduced feature set to predict T-staging, an essential component of the TNM system for cancer diagnosis. Multinomial logistic regression models were developed and evaluated using MSE, AIC, BIC, and Deviance …