Bioinformatics Commons^™

Loss-Of-Function Genomic Variants Highlight Potential Therapeutic Targets For Cardiovascular Disease, Jonas B. Nielsen, Oren Rom, Ida Surakka, Sarah E. Graham, Wei Zhou, Tanmoy Roychowdhury, Lars G. Fritsche, Sarah A. Gagliano Taliun, Carlo Sidore, Yuhao Liu, Maiken E. Gabrielsen, Anne Heidi Skogholt, Brooke Wolford, William Overton, Ying Zhao, Jin Chen, He Zhang, Whitney E. Hornsby, Akua Acheampong, Austen Grooms, Amanda Schaefer, Gregory J. M. Zajac, Luis Villacorta, Jifeng Zhang, Ben Brumpton, Mari Løset, Vivek Rai, Pia R. Lundegaard, Morten S. Olesen, Kent D. Taylor, Donna K. Arnett

Epidemiology and Environmental Health Faculty Publications

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of …

Missing Lateral Relationships In Top‑Level Concepts Of An Ontology, Ling Zheng, Yan Chen, Hua Min, P. Lloyd Hildebrand, Hao Liu, Michael Halper, James Geller, Sherri De Coronado, Yehoshua Perl

Publications and Research

Background: Ontologies house various kinds of domain knowledge in formal structures, primarily in the form of concepts and the associative relationships between them. Ontologies have become integral components of many health information processing environments. Hence, quality assurance of the conceptual content of any ontology is critical. Relationships are foundational to the definition of concepts. Missing relationship errors (i.e., unintended omissions of important definitional relationships) can have a deleterious effect on the quality of an ontology. An abstraction network is a structure that overlays an ontology and provides an alternate, summarization view of its contents. One kind of abstraction network is …

Outlier Concepts Auditing Methodology For A Large Family Of Biomedical Ontologies, Ling Zheng, Hua Min, Yan Chen, Vipina Keloth, James Geller, Yehoshua Perl, George Hripcsak

Publications and Research

Background: Summarization networks are compact summaries of ontologies. The “Big Picture” view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that have outgoing lateral relationships, we have developed the "partial-area taxonomy" summarization network. Prior research has identified one kind of outlier concepts, concepts of small partials-areas within partial-area taxonomies. Previously we have shown that the small partial-area technique works successfully for four ontologies (or their hierarchies).

Methods: To improve the Quality Assurance (QA) scalability, a family-based QA framework, where one QA technique is potentially applicable to …

Pathway‐Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Bagchee‐Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan

Biochemistry Publications

Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi‐gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology‐based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway‐extended SVMs predicted responses in …

Pathway-Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Jem Bagchee-Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan

Biochemistry Publications

No abstract provided.

Genome-Wide Dna Methylation Profiling In Human Breast Tissue By Illumina Truseq Methyl Capture Epic Sequencing And Infinium Methylationepic Beadchip Microarray, Nan Lin, Jinpeng Liu, James Castle, Jun Wan, Aditi Shendre, Yunlong Liu, Chi Wang, Chunyan He

Markey Cancer Center Faculty Publications

A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing. We empirically examined the performance of TruSeq EPIC and EPIC-array in assessing genome-wide DNA methylation in breast tissue samples. TruSeq EPIC provided data with a much higher density in the regions when compared to EPIC-array (~2.74 million CpGs with at least 10X coverage vs ~752 K CpGs, respectively). Approximately 398 K CpGs were common and measured across the two platforms in every sample. Overall, there …

Estimating Partial Body Ionizing Radiation Exposure By Automated Cytogenetic Biodosimetry, Ben Shirley, Peter Rogan

Biochemistry Publications

Purpose: Inhomogeneous exposures to ionizing radiation can be detected and quantified with the dicentric chromosome assay (DCA) of metaphase cells. Complete automation of interpretation of the DCA for whole-body irradiation has significantly improved throughput without compromising accuracy, however, low levels of residual false positive dicentric chromosomes (DCs) have confounded its application for partial-body exposure determination.

Materials and methods: We describe a method of estimating and correcting for false positive DCs in digitally processed images of metaphase cells. Nearly all DCs detected in unirradiated calibration samples are introduced by digital image processing. DC frequencies of irradiated calibration samples and those exposed …

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

Journal Articles

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, …

Integrated Multiparametric Radiomics And Informatics System For Characterizing Breast Tumor Characteristics With The Oncotypedx Gene Assay, Michael A. Jacobs, Christopher B. Umbricht, Vishwa S. Parekh, Riham H. El Khouli, Leslie Cope, Katarzyna J. Macura, Susan Harvey, Antonio C. Wolff

Radiology Faculty Publications

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained …

Atom Identifiers Generated By A Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization Across Metabolic Databases, Huan Jin, Joshua M. Mitchell, Hunter N. B. Moseley

Molecular and Cellular Biochemistry Faculty Publications

Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of …

Mechanism Of Translation Inhibition By Type Ii Gnat Toxin Atat2, Stepan V Ovchinnikov, Dmitry Bikmetov, Alexei Livenskyi, Marina Serebryakova, Brendan Wilcox, Kyle Mangano, Dmitrii I Shiriaev, Ilya A Osterman, Petr V Sergiev, Sergei Borukhov, Nora Vazquez-Laslop, Alexander S Mankin, Konstantin Severinov, Svetlana Dubiley

Rowan-Virtua School of Osteopathic Medicine Faculty Scholarship

Type II toxin-antitoxins systems are widespread in prokaryotic genomes. Typically, they comprise two proteins, a toxin, and an antitoxin, encoded by adjacent genes and forming a complex in which the enzymatic activity of the toxin is inhibited. Under stress conditions, the antitoxin is degraded liberating the active toxin. Though thousands of various toxin-antitoxins pairs have been predicted bioinformatically, only a handful has been thoroughly characterized. Here, we describe the AtaT2 toxin from a toxin-antitoxin system from Escherichia coli O157:H7. We show that AtaT2 is the first GNAT (Gcn5-related N-acetyltransferase) toxin that specifically targets charged glycyl tRNA. In vivo, the AtaT2 …

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

Journal Articles

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 …

A Neutrosophic Clinical Decision-Making System For Cardiovascular Diseases Risk Analysis, Florentin Smarandache, Shaista Habib, Wardat-Us- Salam, M. Arif Butt, Muhammad Akram

Branch Mathematics and Statistics Faculty and Staff Publications

Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirtyfive symptoms as input parameters, which are, gender, …

Β-Amyloid And Tau Drive Early Alzheimer's Disease Decline While Glucose Hypometabolism Drives Late Decline, Tyler C. Hammond, Xin Xing, Chris Wang, David Ma, Kwangsik Nho, Paul K. Crane, Fanny Elahi, David A. Ziegler, Gongbo Liang, Qiang Cheng, Lucille M. Yanckello, Nathan Jacobs, Ai-Ling Lin

Sanders-Brown Center on Aging Faculty Publications

Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ …

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

Journal Articles

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) …

Contributions Of Gyra And Parc Mutations And Qnrs2 Acquisition To Ciprofloxacin Resistance In Aeromonas Veronii Hm21, Daniel J. Silverstein

Honors Scholar Theses

In recent years, ciprofloxacin resistant (CpR) Aeromonas veronii and A. hydrophila strains have been isolated from the wounds of patients receiving leech therapy. Genome comparisons of these CpR isolates revealed the presence of chromosomal mutations in gyrA and parC as well as the gain of qnrS2 on either a large, 34 kb, conjugatable, low-copy plasmid, pAv42, or on a small, 6.8 kb, high-copy plasmid, pAh1471. The minimum inhibitory concentration, MIC, for Cp of these clinical isolates ranged from 1 to ≥32 µg/mL and some harbored a qnrS2 containing plasmid. We wanted to assess the contributions of these factors in an …

Two (Or More) Viruses In One Bat: A Systematic Quantitative Literature Review Of Viral Coinfection In Bats, Eli J. Kaufman

Independent Study Project (ISP) Collection

Viral coinfection is an important topic in pathogen dynamics, and can increase viral shedding and change disease outcomes. As bats are carriers of important zoonoses, such as the SARS coronaviruses, rabies, and other deadly viruses, knowing more about their coinfection dynamics is important. This quantitative systematic literature review sought to show how many papers reported bat viral coinfections, and created three databases. The first database, the SQLR database was based on searches for coinfections. The second database, the Astrovirus database was to determine how much of the literature was being missed by examining a single viral family more in depth …

On The Inadequacy Of Species Distribution Models For Modelling The Spread Of Sars-Cov-2: Response To Araújo And Naimi, Joseph D. Chipperfield, Blas M. Benito, Robert B. O'Hara, Richard J. Telford, Colin J. Carlson

Public Health Resources

The ongoing pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing significant damage to public health and economic livelihoods, and is putting significant strains on healthcare services globally. This unfolding emergency has prompted the preparation and dissemination of the article “Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate” by Araújo and Naimi (2020). The authors present the results of an ensemble forecast made from a suite of species distribution models (SDMs), where they attempt to predict the suitability of the climate for the spread of SARS-CoV-2 over the coming months. They argue that climate is …

Scalable Profiling And Visualization For Characterizing Microbiomes, Camilo Valdes

FIU Electronic Theses and Dissertations

Metagenomics is the study of the combined genetic material found in microbiome samples, and it serves as an instrument for studying microbial communities, their biodiversities, and the relationships to their host environments. Creating, interpreting, and understanding microbial community profiles produced from microbiome samples is a challenging task as it requires large computational resources along with innovative techniques to process and analyze datasets that can contain terabytes of information.

The community profiles are critical because they provide information about what microorganisms are present in the sample, and in what proportions. This is particularly important as many human diseases and environmental disasters …

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

Journal Articles

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%) …

Expression Of Cytokines And Chemokines As Predictors Of Stroke Outcomes In Acute Ischemic Stroke, Sarah R. Martha, Qiang Cheng, Justin F. Fraser, Liyu Gong, Lisa A. Collier, Stephanie M. Davis, Doug Lukins, Abdulnasser Alhajeri, Stephen Grupke, Keith R. Pennypacker

Institute for Biomedical Informatics Faculty Publications

Introduction: Ischemic stroke remains one of the most debilitating diseases and is the fifth leading cause of death in the US. The ability to predict stroke outcomes within the acute period of stroke would be essential for care planning and rehabilitation. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) study collects arterial blood immediately distal and proximal to the intracranial thrombus at the time of mechanical thrombectomy. These blood samples are an innovative resource in evaluating acute gene expression changes at the time of ischemic stroke. The purpose of this study was to identify inflammatory genes and …

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

Journal Articles

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 …