Data Science Commons^™

Charting The Proteome Landscape In Major Psychiatric Disorders: From Biomarkers To Biological Pathways Towards Drug Discovery, Brisa S Fernandes, Yulin Dai, Peilin Jia, Zhongming Zhao

Faculty, Staff and Student Publications

Schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are major mental disorders that affect a significant proportion of the global population. Advancing our knowledge of the pathophysiology of these disorders and identifying biomarkers are urgent needs for developing objective diagnostic tests and new therapeutics. In this study, we performed a systematic review and then extracted, curated, and analyzed proteomics data from published studies, aiming to assess the proteome in peripheral blood of individuals with SZ, BD, or MDD. Then, we performed pathway and network analyses to illuminate the biological themes concatenated by the differentially expressed proteins by systematically …

Real-World Matching Performance Of Deidentified Record-Linking Tokens, Elmer V Bernstam, Reuben Joseph Applegate, Alvin Yu, Deepa Chaudhari, Tian Liu, Alex Coda, Jonah Leshin

Faculty, Staff and Student Publications

OBJECTIVE: Our objective was to evaluate tokens commonly used by clinical research consortia to aggregate clinical data across institutions.

METHODS: This study compares tokens alone and token-based matching algorithms against manual annotation for 20,002 record pairs extracted from the University of Texas Houston's clinical data warehouse (CDW) in terms of entity resolution.

RESULTS: The highest precision achieved was 99.9% with a token derived from the first name, last name, gender, and date-of-birth. The highest recall achieved was 95.5% with an algorithm involving tokens that reflected combinations of first name, last name, gender, date-of-birth, and social security number.

DISCUSSION: To protect …

Development Of A Quality Improvement Dental Chart Review Training Program, Elsbeth Kalenderian, Nutan B Hebballi, Amy Franklin, Alfa Yansane, Ana M Ibarra Noriega, Joel White, Muhammad F Walji

Faculty, Staff and Student Publications

INTRODUCTION: Chart review is central to understanding adverse events (AEs) in medicine. In this article, we describe the process and results of educating chart reviewers assigned to evaluate dental AEs.

METHODS: We developed a Web-based training program, "Dental Patient Safety Training," which uses both independent and consensus-based curricula, for identifying AEs recorded in electronic health records in the dental setting. Training included (1) didactic education, (2) skills training using videos and guided walkthroughs, (3) quizzes with feedback, and (4) hands-on learning exercises. In addition, novice reviewers were coached weekly during consensus review discussions. TeamExpert was composed of 2 experienced reviewers, …

Neural Networks And Stochastic Differential Equations, Stephanie L. Flores

Theses and Dissertations

Influenced by the seminal work, “Physics Informed Neural Networks” by Raissi et al., 2017, there has been a growing interest in solving and parameter estimation of Nonlinear Partial Differential Equations (PDE) with Deep Neural networks in recent years. In fact, this has broadened the pathways and shed light on deep learning of stochastic differential equations (SDE) and stochastic PDE’s (SPDE).In this work, we intend to investigate the current approaches of solving and parameter estimation of the SDE/SPDE with deep neural networks and the possibility of extending them to obtain more accurate/stable solutions with residual systems and/or generative adversarial neural networks. …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Tempering The Adversary: An Exploration Into The Applications Of Game Theoretic Feature Selection And Regression, Stephen Mcgee

All Dissertations

Most modern machine learning algorithms tend to focus on an "average-case" approach, where every data point contributes the same amount of influence towards calculating the fit of a model. This "per-data point" error (or loss) is averaged together into an overall loss and typically minimized with an objective function. However, this can be insensitive to valuable outliers. Inspired by game theory, the goal of this work is to explore the utility of incorporating an optimally-playing adversary into feature selection and regression frameworks. The adversary assigns weights to the data elements so as to degrade the modeler's performance in an optimal …

Unsupervised Contrastive Representation Learning For Knowledge Distillation And Clustering, Fei Ding

All Dissertations

Unsupervised contrastive learning has emerged as an important training strategy to learn representation by pulling positive samples closer and pushing negative samples apart in low-dimensional latent space. Usually, positive samples are the augmented versions of the same input and negative samples are from different inputs. Once the low-dimensional representations are learned, further analysis, such as clustering, and classification can be performed using the representations. Currently, there are two challenges in this framework. First, the empirical studies reveal that even though contrastive learning methods show great progress in representation learning on large model training, they do not work well for small …

Deep Learning For Detecting Trees In The Urban Environment From Lidar, Julian R. Rice

Master's Theses

Cataloguing and classifying trees in the urban environment is a crucial step in urban and environmental planning. However, manual collection and maintenance of this data is expensive and time-consuming. Algorithmic approaches that rely on remote sensing data have been developed for tree detection in forests, though they generally struggle in the more varied urban environment. This work proposes a novel method for the detection of trees in the urban environment that applies deep learning to remote sensing data. Specifically, we train a PointNet-based neural network to predict tree locations directly from LIDAR data augmented with multi-spectral imaging. We compare this …

Spatiotemporal Data Augmentation Of Modis-Landsat Water Bodies Using Generative Adversarial Networks, Ashit Neema

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The monitoring of the shape and area of a water body is an essential component for many Earth science and Hydrological applications. For this purpose, these applications require remote sensing data which provides accurate analysis of the water bodies. In this thesis the same is being attempted, first, a model is created that can map the information from one kind of satellite that captures the data from a distance of 500m to another data that is captured by a different satellite at a distance of 30m. To achieve this, we first collected the data from both of the satellites and …

Optimized Three Deep Learning Models Based-Pso Hyperparameters For Beijing Pm2.5 Prediction, Andri Pranolo, Yingchi Mao, Aji Prasetya Wibawa, Agung Bella Putra Utama, Felix Andika Dwiyanto

Knowledge Engineering and Data Science

Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, …

Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa

Beyond: Undergraduate Research Journal

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational model …

Are Sharks Attracted To Caged Fish And Associated Infrastructure?, Charlie Huveneers, Yuri Niella, Michael Drew, Joshua Dennis, Thomas M. Clarke, Alison Wright, Simon Bryars, Matias Braccini, Chris Dowling, Stephen J. Newman, Paul Butcher, Scott Dalton

Fisheries Research Articles

Huveneers Charlie, Niella Yuri, Drew Michael, Dennis Joshua, Clarke Thomas M., Wright Alison, Bryars Simon, Braccini Matias, Dowling Chris, Newman Stephen J., Butcher Paul, Dalton Scott (2022) Are sharks attracted to caged fish and associated infrastructure?. Marine and Freshwater Research 73, 1404-1410.

https://doi.org/10.1071/MF22039

A Method For Bridging Population-Specific Genotypes To Detect Gene Modules Associated With Alzheimer's Disease, Yulin Dai, Peilin Jia, Zhongming Zhao, Assaf Gottlieb

Faculty, Staff and Student Publications

BACKGROUND: Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success.

METHODS: Here, we introduce a pipeline that combines gene expression imputation with gene module discovery, including a dense gene module search and a gene set variation analysis, to address the transferability issue. Our method feeds association probabilities of imputed gene expression with a selected phenotype into tissue-specific gene-module discovery over protein interaction …

Toward A Standard Formal Semantic Representation Of The Model Card Report, Muhammad Tuan Amith, Licong Cui, Degui Zhi, Kirk Roberts, Xiaoqian Jiang, Fang Li, Evan Yu, Cui Tao

Faculty, Staff and Student Publications

BACKGROUND: Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports.

RESULTS: Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of …

Toward A Standard Formal Semantic Representation Of The Model Card Report, Muhammad Tuan Amith, Licong Cui, Degui Zhi, Kirk Roberts, Xiaoqian Jiang, Fang Li, Evan Yu, Cui Tao

Faculty, Staff and Student Publications

BACKGROUND: Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports.

RESULTS: Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of …

Constrained Pseudorandom Functions From Pseudorandom Synthesizers, Zachary Kissel

Computer and Data Science Faculty Publications

In this paper we resolve the question of whether or not constrained pseudorandom functions (CPRFs) can be built directly from pseudorandom synthesizers. In particular, we demonstrate that the generic PRF construction from pseudorandom synthesizers due to Naor and Reingold can be used to construct CPRFs with bit-fixed predicates using the "direct-line'' approach. We further introduce a property of CPRFs that may be of independent interest.

Aligning The American Health Information Management Association Entry-Level Curricula Competencies And Career Map With Industry Job Postings: Cross-Sectional Study, Susan H Fenton, David T Marc, Angela Kennedy, Debra Hamada, Robert Hoyt, Karima Lalani, Connie Renda, Rebecca B Reynolds

Faculty, Staff and Student Publications

BACKGROUND: The field of health information management (HIM) focuses on the protection and management of health information from a variety of sources. The American Health Information Management Association (AHIMA) Council for Excellence in Education (CEE) determines the needed skills and competencies for this field. AHIMA's HIM curricula competencies are divided into several domains among the associate, undergraduate, and graduate levels. Moreover, AHIMA's career map displays career paths for HIM professionals. What is not known is whether these competencies and the career map align with industry demands.

OBJECTIVE: The primary aim of this study is to analyze HIM job postings on …

A Comparative Study On Deep Learning Models For Text Classification Of Unstructured Medical Notes With Various Levels Of Class Imbalance, Hongxia Lu, Louis Ehwerhemuepha, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background

Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios.

Methods

In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical …

Analysis Of Hawk Mountain Sanctuary Observation Data From 1976 Through 2021, Dale E. Parson

Computer Science and Information Technology Faculty

The primary objective is to correlate climate change data to changes in raptor observations at the Hawk Mountain Sanctuary in northern Berks County, Pennsylvania. Additional objectives include uncovering trends in climate observations at Hawk Mountain's North Lookout and the Allentown Airport throughout the observation period, and to examine trends in raptor observation properties independent of climate changes.

Non-Gaussian Analysis Of Herbarium Specimen Damageto Optimize Specimen Collection Management, Aris Yaman, Yulia Aris Kartika, Ariani Indrawati, Zaenal Akbar, Lindung P. Manik, Wita Wardani, Tutie Djarwaningsih, Taufik Mahendra, Dadan R. Saleh

Knowledge Engineering and Data Science

Damage to specimen collections occurs in practically every herbarium across the world. Hence, some precautions must be taken, such as investigating the factors that cause specimen damage in their collections and evaluating their herbarium collection handling and usage policy. However, manual investigation of the causes of herbarium collection damage requires a lot of effort and time. Only a few studies have attempted to investigate the causes of herbarium collection damage. So far, the non-gaussian approach to detecting the causes of damage to herbarium specimens has not been studied before. This study attempted to explore the effect of species type, time, …