Data Science Commons^™

Geospatial Temporal Crime Prediction Using Convolution And Lstm Neural Networks: Enhancing The Las Vegas Cardiff Model, Corey D. Holmes, Christian Orji, Chris Papesh

SMU Data Science Review

According to the Department of Justice, more than half of violent crimes go unreported to law enforcement in the United States (Kollar et al., 2018). This data gap reduces the opportunity to implement proven solutions in the areas with the greatest need. In 1996, Dr. Shepherd developed the Cardiff Model with the aim of bringing together hospitals, law enforcement, and community leaders through the sharing of data. We partnered with ongoing efforts to implement the Cardiff Model in Las Vegas, Nevada. Our goal was to provide a geospatial temporal model that can predict the next 30 days of crime. By …

Rethinking Retrieval Augmented Fine-Tuning In An Evolving Llm Landscape, Nicholas Sager, Timothy Cabaza, Matthew Cusack, Ryan Bass, Joaquin Dominguez

SMU Data Science Review

This study explores the utilization of Retrieval Augmented Fine-Tuning (RAFT) to enhance the performance of Large Language Models (LLMs) in domain-specific Retrieval Augmented Generation (RAG) tasks. By integrating domain-specific information during the retrieval process, RAG aims to reduce hallucination and improve the accuracy of LLM outputs. We investigate the use of RAFT, an approach that enhances LLMs by incorporating domain-specific knowledge and effectively handling distractor documents. This paper validates previous work, which found that RAFT can considerably improve the performance of Llama2-7B in specific domains. We also expand upon previous work into new state-of-the-art open-source models and other datasets with …

Enhancing Shap With Multi-Core Parallelization And Distributed Computation, Matthew David, William Jones, Hayley Horn

SMU Data Science Review

In recent years, the adoption of complex machine learning algorithms, often perceived as “black box” models, has grown exponentially across various disciplines. However, the lack of understanding regarding how these models come to their predictions often fosters skepticism and mistrust. In response to the demand for transparency and interpretability, Explainable AI techniques, such as SHapley Additive exPlanations (SHAP), have emerged as powerful tools for comprehending and trusting these algorithms. However, SHAP has an exponential computational demand O( x2 ), where x is the number of features. This becomes increasingly problematic with the larger datasets standard in most industries. Many frameworks …

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 …

Improving Large Language Models For Clinical Named Entity Recognition Via Prompt Engineering, Vipina Kuttichi Keloth, Xu Zuo, Yujia Zhou, Zehan Li, Xiaoqian Jiang, Zhiyong Lu, Kirk Roberts, Hua Xu

Faculty, Staff and Student Publications

IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.

OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.

MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, …

Ensemble Pretrained Language Models To Extract Biomedical Knowledge From Literature, Yan Hu, Yao-Shun Chuang, Jianping He, Avisha Das, Vipina Kuttichi Keloth, Yuntao Yang, Chiamaka S Diala, Kirk E Roberts, Cui Tao, Xiaoqian Jiang, W Jim Zheng, Hua Xu

Faculty, Staff and Student Publications

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking.

MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and …

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 …

Who Are You Rooting For? T20 Cricket World Cup 2024, Usa & Wi, Purvesh Desai

Dissertations, Theses, and Capstone Projects

The T20 Cricket World Cup '24 played in USA & WI will bring immense excitement to the cricket lovers around the world and have a question to themselves “Who shall I Root for?” How do people or fans support their favorite teams and on what criteria do they pick these teams will discuss in here.

Patriotism, tradition, and favorite individual players, are the main reasons for the fans to choose their and support the team. Nation is the biggest pride of an individual and many people choose their pride over everything. People celebrate when the home team plays on the …

Advanced Predictive Analytics On Financial Donations To Nonprofit Organizations, Fhamida Keya

Dissertations, Theses, and Capstone Projects

Non-profit organizations rely on donation contributions to carry out their social driven agenda. In the context of fundraising and donor management, it is crucial to uncover complex insights about donor behavior for optimizing strategies and enhancing donor engagement. This project employed advanced analytical techniques on the dataset Donations Received by City Agencies sourced from NYC Open Data by implementing predictive modeling, clustering segmentation, and time series analysis and forecasting. The project will attempt to uncover patterns in donation behavior, identify factors that influence donation amounts, and segment out donor profiles all of which can be leveraged to optimize strategy decisions, …

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

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, 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, 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, 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, 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. …