Data Science Commons^™

Integrated Mrna Sequence Optimization Using Deep Learning, Haoran Gong, Jianguo Wen, Ruihan Luo, Yuzhou Feng, Jingjing Guo, Hongguang Fu, Xiaobo Zhou

Faculty, Staff and Student Publications

The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps: open reading frame (ORF) optimization and 5' untranslated region (UTR) and 3'UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random …

Ontologies Applied In Clinical Decision Support System Rules: Systematic Review, Xia Jing, Hua Min, Yang Gong, Paul Biondich, David Robinson, Timothy Law, Christian Nohr, Arild Faxvaag, Lior Rennert, Nina Hubig, Ronald Gimbel

Faculty, Staff and Student Publications

BACKGROUND: Clinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused.

OBJECTIVE: Ontologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers …

Wrapper-Based Deep Feature Optimization For Activity Recognition In The Wearable Sensor Networks Of Healthcare Systems, Karam Kumar Sahoo, Raghunath Ghosh, Saurav Mallik, Arup Roy, Pawan Kumar Singh, Zhongming Zhao

Faculty, Staff and Student Publications

The Human Activity Recognition (HAR) problem leverages pattern recognition to classify physical human activities as they are captured by several sensor modalities. Remote monitoring of an individual's activities has gained importance due to the reduction in travel and physical activities during the pandemic. Research on HAR enables one person to either remotely monitor or recognize another person's activity via the ubiquitous mobile device or by using sensor-based Internet of Things (IoT). Our proposed work focuses on the accurate classification of daily human activities from both accelerometer and gyroscope sensor data after converting into spectrogram images. The feature extraction process follows …

Scalable Causal Structure Learning: Scoping Review Of Traditional And Deep Learning Algorithms And New Opportunities In Biomedicine, Pulakesh Upadhyaya, Kai Zhang, Can Li, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

BACKGROUND: Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care.

OBJECTIVE: This paper provides a practical review and tutorial on scalable causal structure learning models with examples of real-world data to help health care audiences understand and apply them.

METHODS: We reviewed traditional (combinatorial and score-based) methods for causal structure discovery and machine learning-based schemes. Various traditional approaches have been studied to tackle this problem, the most important among these being the Peter Spirtes and Clark Glymour algorithms. This was followed by analyzing the …

Psychometric Properties Of A Combined Go/No-Go And Continuous Performance Task Across Childhood, Caron A.C. Clark, Kaitlyn Cook, Rui Wang, Michael Rueschman, Jerilynn Radcliffe, Susan Redline, H. Gerry Taylor

Statistical and Data Sciences: Faculty Publications

Despite the critical importance of attention for children’s self-regulation and mental health, there are few task-based measures of this construct appropriate for use across a wide childhood age range including very young children. Three versions of a combined go/no-go and continuous performance task (GNG/CPT) were created with varying length and timing parameters to maximize their appropriateness for age groups spanning early to middle childhood. As part of the baseline assessment of a clinical trial, 452 children aged 3–12 years (50% male, 50% female; 52% White, non-Hispanic, 27% Black, 16% Hispanic/Latinx; 6% other ethnicity/race) completed the task. Confirmatory factor analysis indicated …

Visual Analytics And Modeling Of Materials Property Data, Diwas Bhattarai

LSU Doctoral Dissertations

Due to significant advancements in experimental and computational techniques, materials data are abundant. To facilitate data-driven research, it calls for a system for managing and sharing data and supporting a set of tools for effective data analysis and modeling. Generally, a given material property M can be considered as a multivariate data problem. The dimensions of M are the values of the property itself, the conditions (pressure P, temperature T, and multi-component composition X) that control the concerned property, and relevant metadata I (source, date).

Here we present a comprehensive database considering both experimental and computational sources …

Ageanno: A Knowledgebase Of Single-Cell Annotation Of Aging In Human, Kexin Huang, Hoaran Gong, Jingjing Guan, Lingxiao Zhang, Changbao Hu, Weiling Zhao, Liyu Huang, Wei Zhang, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging process and design targeted anti-aging therapeutics. Here, we built AgeAnno (https://relab.xidian.edu.cn/AgeAnno/#/), a knowledgebase of single cell annotation of aging in human, aiming to provide comprehensive characterizations for aging-related genes across diverse tissue-cell types in human by using single-cell RNA and ATAC sequencing data (scRNA and scATAC). The current version of AgeAnno houses 1 678 610 cells from 28 healthy …

Spascer: Spatial Transcriptomics Annotation At Single-Cell Resolution, Zhiwei Fan, Yangyang Luo, Huifen Lu, Tiangang Wang, Yuzhou Feng, Weiling Zhao, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), …

Crow Search Algorithm With Time Varying Flight Length Strategies For Feature Selection, Mohammed Abdullahi, Abdulhameed Adamu, Ibrahim Hayatu Hassan

Future Computing and Informatics Journal

Feature Selection (FS) is an efficient technique use to get rid of irrelevant, redundant and noisy attributes in high dimensional datasets while increasing the efficacy of machine learning classification. The CSA is a modest and efficient metaheuristic algorithm which has been used to overcome several FS issues. The flight length (fl) parameter in CSA governs crows' search ability. In CSA, fl is set to a fixed value. As a result, the CSA is plagued by the problem of being hoodwinked in local minimum. This article suggests a remedy to this issue by bringing five new concepts of time dependent fl …

Metaenhance: Metadata Quality Improvement For Electronic Theses And Dissertations, Muntabir H. Choudhury, Lamia Salsabil, Himarsha R. Jayanetti, Jian Wu

College of Sciences Posters

Metadata quality is crucial for digital objects to be discovered through digital library interfaces. Although DL systems have adopted Dublin Core to standardize metadata formats (e.g., ETD-MS v1.1¹), the metadata of digital objects may contain incomplete, inconsistent, and incorrect values [1]. Most existing frameworks to improve metadata quality rely on crowdsourced correction approaches, e.g., [2]. Such methods are usually slow and biased toward documents that are more discoverable by users. Artificial intelligence (AI) based methods can be adopted to overcome this limit by automatically detecting, correcting, and canonicalizing the metadata, featuring quick and unbiased responses to document metadata. …

X-Disetrac: Distributed Eye-Tracking With Extended Realities, Bhanuka Mahanama, Sampath Jayarathna

College of Sciences Posters

Humans use heterogeneous collaboration mediums such as in-person, online, and extended realities for day-to-day activities. Identifying patterns in viewpoints and pupillary responses (a.k.a eye-tracking data) provide informative cues on individual and collective behavior during collaborative tasks. Despite the increasing ubiquity of these different mediums, the aggregation and analysis of eye-tracking data in heterogeneous collaborative environments remain unexplored. Our study proposes X-DisETrac: Extended Distributed Eye Tracking, a versatile framework for eye tracking in heterogeneous environments. Our approach tackles the complexity by establishing a platform-agnostic communication protocol encompassing three data streams to simplify data aggregation and …

Prevention Research Center For Healthy Neighborhoods, Madeline Panus

Celebration of Scholarship 2023

No abstract provided.

Variable Selection And Regression Analysis, Emil Agbemade

Data Science and Data Mining

One of the most valuable crop species, maize, has been the subject of genetic study and experimentation for more than a century. However, species that share similarities and differences across a wide spectrum have developed astonishing adaptations as a result of small changes throughout time. Because it is usual practice to determine the genotypes of thousands of single nucleotide polymorphism (SNP) markers for thousands of patients, the data set we are dealing with has an issue with small n and large p. The result of this is that there are noticeably more predictor factors than responder variables. The original data …

Time Series Forecasting For Stock Market Prices, Albert Zhou

Senior Honors Projects

No abstract provided.

The Impact Of Big Data Utilization On Quality Improvement In Inpatient Facilities, Lakyn Hare

Theses, Dissertations and Capstones

Introduction: Poor quality in healthcare has resulted in avoidable patient complications, including readmission rates. Big data in healthcare can be analyzed and built into a tools, with machine learning, to aid in reduced readmission rates and overall positive patient outcomes.

Purpose of the Study: The intention of this study was to evaluate the ways that big data can be analyzed to improve healthcare, specifically readmissions, patient outcomes, and show cost savings. This study examined different ways that big data could be used in concordance with machine learning, including predictive analysis, to make these improvements.

Methodology: The hypothesis was the …

A Multistage Framework For Detection Of Very Small Objects, Duleep Rathgamage Don, Ramazan Aygun, Mahmut Karakaya

Published and Grey Literature from PhD Candidates

Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our …

Classification Of Adult Income Using Decision Tree, Roland Fiagbe

Data Science and Data Mining

Decision tree is a commonly used data mining methodology for performing classification tasks. It is a tree-based supervised machine learning algorithm that is used to classify or make predictions in a path of how previous questions are answered. Generally, the decision tree algorithm categorizes data into branch-like segments that develop into a tree that contains a root, nodes, and leaves. This project seeks to explore the decision tree methodology and apply it to the Adult Income dataset from the UCI Machine Learning Repository, to determine whether a person makes over 50K per year and determine the necessary factors that improve …

Analyzing The Impact Of Health, Economic, And Demographic Factors On Life Expectancy: A Comparative Study Of Developed And Developing Countries, Mahyar Alinejad

Data Science and Data Mining

This study presents a comprehensive analysis of three prominent machine learning regression models—Random Forest, XGBoost, and Support Vector Machine (SVM)—in the context of predictive analysis. Leveraging a carefully curated dataset, we explore the impact of various hyperparameters on model performance through an exhaustive tuning process. The Random Forest and XGBoost models exhibit robust predictive capabilities, with the former revealing notable insights through feature importance visualization. Additionally, SVM, optimized via GridSearchCV, demonstrates competitive performance. Evaluation metrics, including Mean Squared Error and R-squared, facilitate a thorough comparison of model efficacy. Results highlight nuanced strengths and weaknesses, informing practitioners on the suitability of …

Assessing Univariate And Multivariate Normality In Pls-Sem, Kathy Qing Ma, Weiyong Zhang

Information Technology & Decision Sciences Faculty Publications

Partial least squares structural equation modeling (PLS-SEM) has gained popularity among researchers in part due to its relaxed requirement for multivariate normality. One important step in performing structural equation modeling (SEM) is to test the normality assumption. In this paper, we illustrate how to assess univariate and multivariate normality in PLS-SEM using WarpPLS.

Detecting Overlapping Gene Regions Using The U-Net Attention Mechanism, Samuel Lemma

All Graduate Theses, Dissertations, and Other Capstone Projects

The current issue of locating, diagnosing, and treating cancer and other diseases linked to specific target genes necessitates the creation of a reliable system for precisely identifying target genes that are initially extracted from a human chromosome. Current methodologies often suffer from overlapping gene regions in the target gene that occurs during the analysis process, which can have a substantial impact on the accuracy of the results. Our recommended approach, which was the appropriate model to apply for this particular problem, is set to enhance the analytical process by utilizing neural networks' U-Net with an attention mechanism. We were able …