Data Science Commons^™

Getting Started Analyzing Data In Spss, Kristi Thompson

Western Libraries Presentations

SPSS is a popular package for analyzing data. This session will discuss how to get started on a simple quantitative analysis project using SPSS. Topics covered will include getting summary statistics, creating and modifying variables, creating graphs, running simple analyses, and interpreting SPSS output.

Lstm-Sdm: An Integrated Framework Of Lstm Implementation For Sequential Data Modeling[Formula Presented], Hum Nath Bhandari, Binod Rimal, Nawa Raj Pokhrel, Ramchandra Rimal, Keshab R. Dahal

Arts & Sciences Faculty Publications

LSTM-SDM is a python-based integrated computational framework built on the top of Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented functionalities for implementing single layer and multilayer LSTM models for sequential data modeling and time series forecasting. Multiple subroutines are blended to create a conducive user-friendly environment that facilitates data exploration and visualization, normalization and input preparation, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. We utilized the LSTM-SDM framework in predicting the stock market index and observed impressive results. The framework can be generalized to solve several other real-world time series problems.

Marginal Proportional Hazards Models For Clustered Interval-Censored Data With Time-Dependent Covariates, Kaitlyn Cook, Wenbin Lu, Rui Wang

Statistical and Data Sciences: Faculty Publications

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana’s national adoption of a universal test-and-treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy (i) modified the observed preventative effects of the study intervention and (ii) was associated with a reduction in the population-level incidence of HIV in Botswana. To address these questions, we propose a stratified proportional hazards model for clustered intervalcensored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on …

Policy-Based Redactable Signatures, Zachary Kissel

Computer and Data Science Faculty Publications

In this work we make progress towards solving an open problem posed by Bilzhause et. al, to give constructions of redactable signature schemes that allow the signer to limit the possible redactions performed by a third party. A separate, but related notion, called controlled disclosure allows a redactor to limit future redactions. We look at two types of data, sets and linear data (data organized as a sequence). In the case of sets, we limit redactions using a policy modeled by a monotone circuit or any circuit depending on the size of the universe the set is drawn from. In …

The Impact Of Covid-19 On Opioid-Related Overdose Deaths In Texas, Karima Lalani, Christine Bakos-Block, Marylou Cardenas-Turanzas, Sarah Cohen, Bhanumathi Gopal, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

Prior to the COVID-19 pandemic, the United States was facing an epidemic of opioid overdose deaths, clouding accurate inferences about the impact of the pandemic at the population level. We sought to determine the existence of increases in the trends of opioid-related overdose (ORO) deaths in the Greater Houston metropolitan area from January 2015 through December 2021, and to describe the social vulnerability present in the geographic location of these deaths. We merged records from the county medical examiner's office with social vulnerability indexes (SVIs) for the region and present geospatial locations of the aggregated ORO deaths. Time series analyses …

Scgwas: Landscape Of Trait-Cell Type Associations By Integrating Single-Cell Transcriptomics-Wide And Genome-Wide Association Studies, Peilin Jia, Ruifeng Hu, Fangfang Yan, Yulin Dai, Zhongming Zhao

Faculty, Staff and Student Publications

BACKGROUND: The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes.

RESULTS: scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in …

Federated Learning Algorithms For Generalized Mixed-Effects Model (Glmm) On Horizontally Partitioned Data From Distributed Sources, Wentao Li, Jiayi Tong, Md Monowar Anjum, Noman Mohammed, Yong Chen, Xiaoqian Jiang

Faculty, Staff and Student Publications

OBJECTIVES: This paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. And showed the two proposed methods can perform well in revealing the significance of parameter in distributed datasets, comparing to a centralized GLMM algorithm from R package ('lme4') as the baseline model.

METHODS: The log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation, abbreviated as LA and GH), which supports federated decomposition of GLMM to bring computation to data. To solve …

An Investigation Of The Reconstruction Capacity Of Stacked Convolutional Autoencoders For Log-Mel-Spectrograms, Anastasia Natsiou, Luca Longo, Seán O'Leary

Conference Papers

In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. These representations can be used to manipulate the timbre and influence the synthesis of creative instrumental notes. Modern algorithms, such as neural networks, have inspired the development of expressive synthesizers based on musical instrument timbre compression. Unsupervised deep learning methods can achieve audio compression by training the network to learn a mapping from waveforms or spectrograms to low-dimensional representations. This study investigates the use of stacked convolutional autoencoders for the compression of time-frequency audio representations for a variety of instruments for a single …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Preparation Of Core-Shell-Structured Rdx@Pvdf Microspheres With Improved Thermal Stability And Decreased Mechanical Sensitivity, Hulin Wu, Aifeng Jiang, Mengru Li, Yanyan Wang, Fangchao Zhao, Yanchun Li

Faculty, Staff and Student Publications

Reducing the sensitivity of high-energy simple explosives is the key technology in improving the practical application of high-energy insensitive powder. As the most widely used high-energy explosive, hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is limited in application due to its high sensitivity. In this work, polyvinylidene fluoride (PVDF) was used as an energetic binder. Core-shell-structured RDX@PVDF microspheres are produced using electrospray assembly technology and fully characterized by thermogravimetric analysis, X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive spectroscopy, and mechanical sensitivity. Their thermal stability and mechanical sensitivity are directly related to the weight fraction of the added PVDF. Moreover, core-shell-structured RDX@PVDF microspheres …

Clinical Decision Support Malfunctions Related To Medication Routes: A Case Series, Adam Wright, Scott Nelson, David Rubins, Richard Schreiber, Dean F Sittig

Faculty, Staff and Student Publications

OBJECTIVE: To identify common medication route-related causes of clinical decision support (CDS) malfunctions and best practices for avoiding them.

MATERIALS AND METHODS: Case series of medication route-related CDS malfunctions from diverse healthcare provider organizations.

RESULTS: Nine cases were identified and described, including both false-positive and false-negative alert scenarios. A common cause was the inclusion of nonsystemically available medication routes in value sets (eg, eye drops, ear drops, or topical preparations) when only systemically available routes were appropriate.

DISCUSSION: These value set errors are common, occur across healthcare provider organizations and electronic health record (EHR) systems, affect many different types of …

Complement Component C4 Structural Variation And Quantitative Traits Contribute To Sex-Biased Vulnerability In Systemic Sclerosis, Martin Kerick, Marialbert Acosta-Herrera, Carmen Pilar Simeón-Aznar, José Luis Callejas, Shervin Assassi, Susanna M Proudman, Mandana Nikpour, Nicolas Hunzelmann, Gianluca Moroncini, Jeska K De Vries-Bouwstra, Gisela Orozco, Anne Barton, Ariane L Herrick, Chikashi Terao, Yannick Allanore, Carmen Fonseca, Marta Eugenia Alarcón-Riquelme, Timothy R D J Radstake, Lorenzo Beretta, Christopher P Denton, Maureen D Mayes, Javier Martin

Faculty, Staff and Student Publications

Copy number (CN) polymorphisms of complement C4 play distinct roles in many conditions, including immune-mediated diseases. We investigated the association of C4 CN with systemic sclerosis (SSc) risk. Imputed total C4, C4A, C4B, and HERV-K CN were analyzed in 26,633 individuals and validated in an independent cohort. Our results showed that higher C4 CN confers protection to SSc, and deviations from CN parity of C4A and C4B augmented risk. The protection contributed per copy of C4A and C4B differed by sex. Stronger protection was afforded by C4A in men and by C4B in women. C4 CN correlated well with its …

Analyzing The Production And Use Of Fossil Fuels: A Case For Data Mining And Gis, Alejandro Conde

Geography and the Environment: Graduate Student Capstones

As technology progresses and data grows both larger and more complex, techniques are being developed to keep up with the exponential growth of information. The term “data mining” is a blanket term used to describe an approach to find anomalies and correlations in a large dataset. This approach involves leveraging data mining software to manipulate and prepare data, apply statistics to quantify trends and characteristics in the data from a high level, and potentially apply advanced techniques like machine learning to identify patterns that wouldn’t be apparent otherwise. In this case study, data mining aided a GIS in displaying substantial …

Public Acceptance Of Medical Screening Recommendations, Safety Risks, And Implied Liabilities Requirements For Space Flight Participation, Cory J. Trunkhill

Doctoral Dissertations and Master's Theses

The space tourism industry is preparing to send space flight participants on orbital and suborbital flights. Space flight participants are not professional astronauts and are not subject to the rules and guidelines covering space flight crewmembers. This research addresses public acceptance of current Federal Aviation Administration guidance and regulations as designated for civil participation in human space flight.

The research utilized an ordinal linear regression analysis of survey data to explore the public acceptance of the current medical screening recommended guidance and the regulations for safety risk and implied liability for space flight participation. Independent variables constituted participant demographic representations …

Development Of The Invasive Candidiasis Discharge [I Can Discharge] Model: A Mixed Methods Analysis, Jinhee Jo, Truc T Tran, Nicholas D Beyda, Debora Simmons, Joshua A Hendrickson, Masaad Saeed Almutairi, Faris S Alnezary, Anne J Gonzales-Luna, Edward J Septimus, Kevin W Garey

Faculty, Staff and Student Publications

Patients with invasive candidiasis (IC) have complex medical and infectious disease problems that often require continued care after discharge. This study aimed to assess echinocandin use at hospital discharge and develop a transition of care (TOC) model to facilitate discharge for patients with IC. This was a mixed method study design that used epidemiologic assessment to better understand echinocandin use at hospital discharge TOC. Using grounded theory methodology focused on patients given echinocandins during their last day of hospitalization, a TOC model for patients with IC, the invasive candidiasis [I Can] discharge model was developed to better understand discharge barriers. …

Diagnostic Value Of Ultrasound In Children With Transverse Testicular Ectopia, Wei Zhou, Shoulin Li, Hao Wang, Jianchun Yin, Xiaodong Liu, Junhai Jiang, Guanglun Zhou, Jianguo Wen

Faculty, Staff and Student Publications

OBJECTIVE: The study aimed to investigate the diagnostic value of ultrasound in children's transverse testicular ectopia (TTE).

MATERIALS AND METHODS: We retrospectively studies all TTE cases diagnosed in our hospital from January 2017 to December 2021. All cases were evaluated by ultrasound examination and compared to physical examination and diagnostic laparoscopy results.

RESULTS: This study included 14 TTE patients in total, with a median age was 1.08 years. In the 14 TTE, physical examination found 10 TTE cases, of which nine testes were located in the opposite scrotum, one testis was located in the opposite groin, and the other four …

Aging After Stroke: How To Define Post-Stroke Sarcopenia And What Are Its Risk Factors?, Sheng Li, Javier Gonzalez-Buonomo, Jaskiran Ghuman, Xinran Huang, Aila Malik, Nuray Yozbatiran, Elaine Magat, Gerard E Francisco, Hulin Wu, Walter R Frontera

Faculty, Staff and Student Publications

BACKGROUND: Sarcopenia, generally described as "aging-related loss of skeletal muscle mass and function", can occur secondary to a systemic disease.

AIM: This project aimed to study the prevalence of sarcopenia in chronic ambulatory stroke survivors and its associated risk factors using the two most recent diagnostic criteria.

DESIGN: A cross-sectional observational study.

SETTING: A scientific laboratory.

POPULATION: Chronic stroke.

METHODS: Twenty-eight ambulatory chronic stroke survivors (12 females; mean age=57.8±11.8 years; time after stroke=76±45 months), hand-grip strength, gait speed, and appendicular skeletal muscle mass (ASM) were measured to define sarcopenia. Risk factors, including motor impairment and spasticity, were identified using regression …

Svat: Secure Outsourcing Of Variant Annotation And Genotype Aggregation, Miran Kim, Su Wang, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

BACKGROUND: Sequencing of thousands of samples provides genetic variants with allele frequencies spanning a very large spectrum and gives invaluable insight into genetic determinants of diseases. Protecting the genetic privacy of participants is challenging as only a few rare variants can easily re-identify an individual among millions. In certain cases, there are policy barriers against sharing genetic data from indigenous populations and stigmatizing conditions.

RESULTS: We present SVAT, a method for secure outsourcing of variant annotation and aggregation, which are two basic steps in variant interpretation and detection of causal variants. SVAT uses homomorphic encryption to encrypt the data at …

The Link Between Democratic Institutions And Population Health In The American States, Julianna Pacheco, Scott Lacombe

Government: Faculty Publications

Context: This project investigates the role of state-level institutions in explaining variation in population health in the American states. Although cross-national research has established the positive effects of democracy on population health, little attention has been given to subnational units. The authors leverage a new data set to understand how political accountability and a system of checks and balances are associated with state population health. Methods: The authors estimate error correction models and two-way fixed effects models to estimate how the strength of state-level democratic institutions is associated with infant mortality rates, life expectancy, and midlife mortality. Findings: The authors …

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of `depression', our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …

A Data Driven Modeling Approach For Store Distributed Load And Trajectory Prediction, Nicholas Peters

Doctoral Dissertations and Master's Theses

The task of achieving successful store separation from aircraft and spacecraft has historically been and continues to be, a critical issue for the aerospace industry. Whether it be from store-on-store wake interactions, store-parent body interactions or free stream turbulence, a failed case of store separation poses a serious risk to aircraft operators. Cases of failed store separation do not simply imply missing an intended target, but also bring the risk of collision with, and destruction of, the parent body vehicle. Given this risk, numerous well-tested procedures have been developed to help analyze store separation within the safe confines of wind …

Identifying Candidate Genes And Drug Targets For Alzheimer’S Disease By An Integrative Network Approach Using Genetic And Brain Region-Specific Proteomic Data, Andi Liu, Astrid M Manuel, Yulin Dai, Brisa S Fernandes, Nitesh Enduru, Peilin Jia, Zhongming Zhao

Faculty, Staff and Student Publications

Genome-wide association studies (GWAS) have identified more than 75 genetic variants associated with Alzheimer's disease (ad). However, how these variants function and impact protein expression in brain regions remain elusive. Large-scale proteomic datasets of ad postmortem brain tissues have become available recently. In this study, we used these datasets to investigate brain region-specific molecular pathways underlying ad pathogenesis and explore their potential drug targets. We applied our new network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS), to integrate ad GWAS statistics of 472 868 individuals with proteomic profiles from two brain regions from two large-scale ad cohorts [parahippocampal gyrus …

Video-Urodynamics Efficacy Of Sacral Neuromodulation For Neurogenic Bladder Guided By Three-Dimensional Imaging Ct And C-Arm Fluoroscopy: A Single-Center Prospective Study, Shuaishuai Shan, Wen Zhu, Guoxian Zhang, Qinyong Zhang, Yingyu Che, Jianguo Wen, Qingwei Wang

Faculty, Staff and Student Publications

To assess the efficacy of sacral neuromodulation (SNM) for neurogenic bladder (NB), guided by intraoperative three-dimensional imaging of sacral computed tomography (CT) and mobile C-arm fluoroscopy through video-urodynamics examination. We enrolled 52 patients with NB who underwent conservative treatment with poor results between September 2019 and June 2021 and prospectively underwent SNM guided by intraoperative three-dimensional imaging of sacral CT and mobile C-arm fluoroscopy. Video-urodynamics examination, voiding diary, quality of life questionnaire, overactive bladder symptom scale (OABSS) scoring, and bowel dysfunction exam were completed and recorded at baseline, at SNM testing, and at 6-month follow-up phases. Finally, we calculated the …

Molecular Pathways Enhance Drug Response Prediction Using Transfer Learning From Cell Lines To Tumors And Patient-Derived Xenografts, Yi-Ching Tang, Reid T Powell, Assaf Gottlieb

Faculty, Staff and Student Publications

Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features. We further compute feature importance to identify pathways most important to drug response prediction. We obtained good performance on tumors (AUROC = 0.77) and patient derived xenografts from triple negative breast cancers (RMSE = 0.11). Using feature importance, …

Phishing Detection Using Natural Language Processing And Machine Learning, Apurv Mittal, Dr Daniel Engels, Harsha Kommanapalli, Ravi Sivaraman, Taifur Chowdhury

SMU Data Science Review

Phishing emails are a primary mode of entry for attackers into an organization. A successful phishing attempt leads to unauthorized access to sensitive information and systems. However, automatically identifying phishing emails is often difficult since many phishing emails have composite features such as body text and metadata that are nearly indistinguishable from valid emails. This paper presents a novel machine learning-based framework, the DARTH framework, that characterizes and combines multiple models, with one model for each composite feature, that enables the accurate identification of phishing emails. The framework analyses each composite feature independently utilizing a multi-faceted approach using Natural Language …

Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti

SMU Data Science Review

Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new …

Short Term Forecasting Of Solar Radiation, Ashwin Thota, Bradley Blanchard, Lijju Mathew, Paritosh Rai, Sid Swarupananda

SMU Data Science Review

This paper details how to predict solar radiation at a location for the next few hours using machine learning techniques like Facebook’s Prophet, and Amazon’s DeepAR+. Multiple techniques like AutoRegressive (ARIMA) and Exponential Smoothing (ES) have been used to forecast solar radiation, but they lack accuracy and are not scalable. Whereas Prophet, and Amazon’s DeepAR+ are scalable, accurate, and easily integrated into other machine learning techniques. This will be the first time where the combination of these techniques along with Linear Regression, Random Forest, XGBoost and Decision Tree will be leveraged to forecast solar radiation for the short term. Predicting …

Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback, Chris Roche, Nathan Deinlein, Darryl Dawkins, Faizan Javed

SMU Data Science Review

For English teachers and students who are dissatisfied with the one-size-fits-all approach of current Automated Essay Scoring (AES) systems, this research uses Natural Language Processing (NLP) techniques that provide a focus on configurability and interpretability. Unlike traditional AES models which are designed to provide an overall score based on pre-trained criteria, this tool allows teachers to tailor feedback based upon specific focus areas. The tool implements a user-interface that serves as a customizable rubric. Students’ essays are inputted into the tool either by the student or by the teacher via the application’s user-interface. Based on the rubric settings, the tool …

Stock Forecasts With Lstm And Web Sentiment, Michael Burgess, Faizan Javed, Nnenna Okpara, Chance Robinson

SMU Data Science Review

Traditional time-series techniques, such as auto-regressive and moving average models, can have difficulties when applied to stock data due to the randomness inherent to the markets. In this study, Long Short-Term Memory Recurrent Neural Networks, or LSTMs, have been applied to pricing data along with sentiment scores derived from web sources such as Twitter and other financial media outlets. The project team utilized this approach to complement the technical indicators observed at the end of each trading day for three stocks from the NASDAQ stock exchange over a 12-year span. A common benchmark to assess model performance on time series …

Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan

SMU Data Science Review

Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …