Biomedical Informatics Commons^™

Security And Privacy When Applying Fair Principles To Genomic Information, Jaime Delgado, Silvia Llorente

Faculty, Staff and Student Publications

Making data Findable, Accessible, Interoperable and Reusable (FAIR) is a good approach when data needs to be shared. However, security and privacy are still critical aspects. In the FAIRification process, there is a need both for de-identification of data and for license attribution. The paper analyses some of the issues related to this process when the objective is sharing genomic information. The main results are the identification of the already existing standards that could be used for this purpose and how to combine them. Nevertheless, the area is quickly evolving and more specific standards could be specified.

Determining The Utility Of Hl7® Fast Healthcare Interoperability Resources (Fhir®) Standards In Supporting Ehr And Edc-Agnostic Esource Implementations For Clinical Research, Maryam Garza

Dissertations and Theses (Open Access)

Advances in efficiency while maintaining or improving quality are immediately needed in clinical research, specifically for multicenter clinical studies. Though recent evidence points toward electronic health record (EHR) to electronic data capture (EDC) system (EHR to EDC) data collection as a viable contribution, there are many unanswered process and outcome-level questions regarding quality, site burden, and cost within the context of multicenter clinical trials. Direct extraction and use of EHR data in multicenter clinical studies is a long-term and multifaceted endeavor that includes design, development, implementation and evaluation of methods and tools for semi-automating tasks in the research data collection …

Representation Of Ehr Data For Predictive Modeling: A Comparison Between Umls And Other Terminologies, Laila Rasmy, Firat Tiryaki, Yujia Zhou, Yang Xiang, Cui Tao, Hua Xu, Degui Zhi

Faculty, Staff and Student Publications

OBJECTIVE: Predictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning.

MATERIALS AND METHODS: We projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, …

Improvement Of Cryo-Em Maps By Density Modification, Thomas C Terwilliger, Steven J Ludtke, Randy J Read, Paul D Adams, Pavel V Afonine

Faculty, Staff and Students Publications

A density-modification procedure for improving maps from single-particle electron cryogenic microscopy (cryo-EM) is presented. The theoretical basis of the method is identical to that of maximum-likelihood density modification, previously used to improve maps from macromolecular X-ray crystallography. Key differences from applications in crystallography are that the errors in Fourier coefficients are largely in the phases in crystallography but in both phases and amplitudes in cryo-EM, and that half-maps with independent errors are available in cryo-EM. These differences lead to a distinct approach for combination of information from starting maps with information obtained in the density-modification process. The density-modification procedure was …

Understanding Spatial Language In Radiology: Representation Framework, Annotation, And Spatial Relation Extraction From Chest X-Ray Reports Using Deep Learning, Surabhi Datta, Yuqi Si, Laritza Rodriguez, Sonya E Shooshan, Dina Demner-Fushman, Kirk Roberts

Faculty, Staff and Student Publications

Radiology reports contain a radiologist's interpretations of images, and these images frequently describe spatial relations. Important radiographic findings are mostly described in reference to an anatomical location through spatial prepositions. Such spatial relationships are also linked to various differential diagnoses and often described through uncertainty phrases. Structured representation of this clinically significant spatial information has the potential to be used in a variety of downstream clinical informatics applications. Our focus is to extract these spatial representations from the reports. For this, we first define a representation framework based on the Spatial Role Labeling (SpRL) scheme, which we refer to as …

Critical Micrornas And Regulatory Motifs In Cleft Palate Identified By A Conserved Mirna-Tf-Gene Network Approach In Humans And Mice, Aimin Li, Peilin Jia, Saurav Mallik, Rong Fei, Hiroki Yoshioka, Akiko Suzuki, Junichi Iwata, Zhongming Zhao

Faculty, Staff and Student Publications

Cleft palate (CP) is the second most common congenital birth defect. The etiology of CP is complicated, with involvement of various genetic and environmental factors. To investigate the gene regulatory mechanisms, we designed a powerful regulatory analytical approach to identify the conserved regulatory networks in humans and mice, from which we identified critical microRNAs (miRNAs), target genes and regulatory motifs (miRNA-TF-gene) related to CP. Using our manually curated genes and miRNAs with evidence in CP in humans and mice, we constructed miRNA and transcription factor (TF) co-regulation networks for both humans and mice. A consensus regulatory loop (miR17/miR20a-FOXE1-PDGFRA) and eight …

Rna-Gps Predicts High-Resolution Rna Subcellular Localization And Highlights The Role Of Splicing, Kevin E Wu, Kevin R Parker, Furqan M Fazal, Howard Y Chang, James Zou

Faculty, Staff and Students Publications

Subcellular localization is essential to RNA biogenesis, processing, and function across the gene expression life cycle. However, the specific nucleotide sequence motifs that direct RNA localization are incompletely understood. Fortunately, new sequencing technologies have provided transcriptome-wide atlases of RNA localization, creating an opportunity to leverage computational modeling. Here we present RNA-GPS, a new machine learning model that uses nucleotide-level features to predict RNA localization across eight different subcellular locations-the first to provide such a wide range of predictions. RNA-GPS's design enables high-throughput sequence ablation and feature importance analyses to probe the sequence motifs that drive localization prediction. We find localization …

Covid-19 Testnorm: A Tool To Normalize Covid-19 Testing Names To Loinc Codes, Xiao Dong, Jianfu Li, Ekin Soysal, Jiang Bian, Scott L Duvall, Elizabeth Hanchrow, Hongfang Liu, Kristine E Lynch, Michael Matheny, Karthik Natarajan, Lucila Ohno-Machado, Serguei Pakhomov, Ruth Madeleine Reeves, Amy M Sitapati, Swapna Abhyankar, Theresa Cullen, Jami Deckard, Xiaoqian Jiang, Robert Murphy, Hua Xu

Faculty, Staff and Student Publications

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) …

Proteolysis-Targeting Chimera (Protac) For Targeted Protein Degradation And Cancer Therapy, Xin Li, Yongcheng Song

Faculty, Staff and Students Publications

Proteolysis-targeting chimera (PROTAC) has been developed to be a useful technology for targeted protein degradation. A bifunctional PROTAC molecule consists of a ligand (mostly small-molecule inhibitor) of the protein of interest (POI) and a covalently linked ligand of an E3 ubiquitin ligase (E3). Upon binding to the POI, the PROTAC can recruit E3 for POI ubiquitination, which is subjected to proteasome-mediated degradation. PROTAC complements nucleic acid-based gene knockdown/out technologies for targeted protein reduction and could mimic pharmacological protein inhibition. To date, PROTACs targeting ~ 50 proteins, many of which are clinically validated drug targets, have been successfully developed with several …

Tortuosity-Powered Microfluidic Device For Assessment Of Thrombosis And Antithrombotic Therapy In Whole Blood, David J Luna, Navaneeth K R Pandian, Tanmay Mathur, Justin Bui, Pranav Gadangi, Vadim V Kostousov, Shiu-Ki Rocky Hui, Jun Teruya, Abhishek Jain

Faculty, Staff and Students Publications

Accurate assessment of blood thrombosis and antithrombotic therapy is essential for the management of patients in a variety of clinical conditions, including surgery and on extracorporeal life support. However, current monitoring devices do not measure the effects of hemodynamic forces that contribute significantly to coagulation, platelet function and fibrin formation. This limits the extent to which current assays can predict clotting status in patients. Here, we demonstrate that a biomimetic microfluidic device consisting stenosed and tortuous arteriolar vessels would analyze blood clotting under flow, while requiring a small blood volume. When the device is connected to an inline pressure sensor …

Deep Learning In Clinical Natural Language Processing: A Methodical Review, Stephen Wu, Kirk Roberts, Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, Qiong Wang, Qiang Wei, Yang Xiang, Bo Zhao, Hua Xu

Faculty, Staff and Student Publications

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research.

MATERIALS AND METHODS: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers.

RESULTS: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) …

A Broad-Spectrum Antiviral Molecule, Ql47, Selectively Inhibits Eukaryotic Translation, Mélissanne De Wispelaere, Margot Carocci, Dominique J Burri, William J Neidermyer, Calla M Olson, Imme Roggenbach, Yanke Liang, Jinhua Wang, Sean P J Whelan, Nathanael S Gray, Priscilla L Yang

Faculty, Staff and Students Publications

Small-molecule inhibitors of translation are critical tools to study the molecular mechanisms of protein synthesis. In this study, we sought to characterize how QL47, a host-targeted, small-molecule antiviral agent, inhibits steady-state viral protein expression. We demonstrate that this small molecule broadly inhibits both viral and host protein synthesis and targets a translation step specific to eukaryotic cells. We show that QL47 inhibits protein neosynthesis initiated by both canonical cap-driven and noncanonical initiation strategies, most likely by targeting an early step in translation elongation. Our findings thus establish QL47 as a new small-molecule inhibitor that can be utilized to probe the …

Metaproteomic Analysis Of Human Gut Microbiome In Digestive And Metabolic Diseases, Sheng Pan, Ru Chen

Faculty, Staff and Students Publications

Metaproteomics, as a subfield of proteomics, has quickly emerged as a pivotal tool for global characterization of a microbiome system at a functional level. It has been increasingly applied in studying human digestive and metabolic diseases, and provides information-rich data to identify the dysbiosis of human gut microbiome related to healthy or disease states to elucidate the molecular events underlying host-microbiota interplays. While significant technical challenges still exist, this emerging technology has been demonstrated to provide essential information in interrogating functional changes in the human gut microbiome, complementary to metagenomics and metatranscriptomics. This chapter overviews the overall metaproteomic work flow …

Prospects And Challenges Of Population Health With Online And Other Big Data In Africa; Understanding The Link To Improving Healthcare Service Delivery, Rowland Edet, Bolarinwa Afolabi

Department of Sociology: Faculty Publications

Big data analytics offers promises to many health care service challenges and can provide answers to many population health issues. Big data is having a positive impact in almost every sphere of life in more advanced world while developing countries are striving to meet up. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa and identify its peculiar needs. The purpose of this review was to summarize the challenges faced by …

Digilego For Peripartum Depression: A Novel Patient-Facing Digital Health Instantiation, J Rodin, C Timko, S Harris

Faculty, Staff and Student Publications

Digital health technologies offer unique opportunities to improve health outcomes for mental health conditions such as peripartum depression (PPD), a disorder that affects approximately 10-15% of women in the U.S. every year. In this paper, we present the adaption of a digital technology development framework, Digilego, in the context of PPD. Methods include mapping of the Behavior Intervention Technology (BIT) model and the Patient Engagement Framework (PEF) to translate patient needs captured through focus groups. This informs formative development and implementation of digital health features for optimal patient engagement in PPD screening and management. Results show an array ofPPD-specific Digilego …

Causal Discovery In Radiographic Markers Of Knee Osteoarthritis And Prediction For Knee Osteoarthritis Severity With Attention-Long Short-Term Memory, Yanfei Wang, Lei You, Jacqueline Chyr, Lan Lan, Weiling Zhao, Yujia Zhou, Hua Xu, Philip Noble, Xiaobo Zhou

Faculty, Staff and Student Publications

The goal of this study is to build a prognostic model to predict the severity of radiographic knee osteoarthritis (KOA) and to identify long-term disease progression risk factors for early intervention and treatment. We designed a long short-term memory (LSTM) model with an attention mechanism to predict Kellgren/Lawrence (KL) grade for knee osteoarthritis patients. The attention scores reveal a time-associated impact of different variables on KL grades. We also employed a fast causal inference (FCI) algorithm to estimate the causal relation of key variables, which will aid in clinical interpretability. Based on the clinical information of current visits, we accurately …

Non-Uniformity Of Projection Distributions Attenuates Resolution In Cryo-Em, Philip R Baldwin, Dmitry Lyumkis

Faculty, Staff and Students Publications

Virtually all single-particle cryo-EM experiments currently suffer from specimen adherence to the air-water interface, leading to a non-uniform distribution in the set of projection views. Whereas it is well accepted that uniform projection distributions can lead to high-resolution reconstructions, non-uniform (anisotropic) distributions can negatively affect map quality, elongate structural features, and in some cases, prohibit interpretation altogether. Although some consequences of non-uniform sampling have been described qualitatively, we know little about how sampling quantitatively affects resolution in cryo-EM. Here, we show how inhomogeneity in any projection distribution scheme attenuates the global Fourier Shell Correlation (FSC) in relation to the number …