Biomedical Informatics Commons^™

Quantification Of Infarct Core Signal Using Ct Imaging In Acute Ischemic Stroke, Uma Maria Lal-Trehan Estrada, Grant Meeks, Sergio Salazar-Marioni, Fabien Scalzo, Mudassir Farooqui, Juan Vivanco-Suarez, Santiago Ortega Gutierrez, Sunil A Sheth, Luca Giancardo

Faculty, Staff and Student Publications

In stroke care, the extent of irreversible brain injury, termed infarct core, plays a key role in determining eligibility for acute treatments, such as intravenous thrombolysis and endovascular reperfusion therapies. Many of the pivotal randomized clinical trials testing those therapies used MRI Diffusion-Weighted Imaging (DWI) or CT Perfusion (CTP) to define infarct core. Unfortunately, these modalities are not available 24/7 outside of large stroke centers. As such, there is a need for accurate infarct core determination using faster and more widely available imaging modalities including Non-Contrast CT (NCCT) and CT Angiography (CTA). Prior studies have suggested that CTA provides improved …

Brain Antigens Stimulate Proliferation Of T Lymphocytes With A Pathogenic Phenotype In Multiple Sclerosis Patients, Assaf Gottlieb, Hoai Phuong T Pham, John William Lindsey

Faculty, Staff and Student Publications

A method to stimulate T lymphocytes with a broad range of brain antigens would facilitate identification of the autoantigens for multiple sclerosis and enable definition of the pathogenic mechanisms important for multiple sclerosis. In a previous work, we found that the obvious approach of culturing leukocytes with homogenized brain tissue does not work because the brain homogenate suppresses antigen-specific lymphocyte proliferation. We now report a method that substantially reduces the suppressive activity. We used this non-suppressive brain homogenate to stimulate leukocytes from multiple sclerosis patients and controls. We also stimulated with common viruses for comparison. We measured proliferation, selected the …

Electron Transfer Dynamics And Electrocatalytic Oxygen Evolution Activities Of The Co3o4 Nanoparticles Attached To Indium Tin Oxide By Self-Assembled Monolayers, Xuan Liu, Qianhong Tian, Yvpei Li, Zixiang Zhou, Jinlian Wang, Shuling Liu, Chao Wang

Faculty, Staff and Student Publications

The Co3O4 nanoparticle-modified indium tin oxide-coated glass slide (ITO) electrodes are successfully prepared using dicarboxylic acid as the self-assembled monolayer through a surface esterification reaction. The ITO-SAM-Co3O4 (SAM = dicarboxylic acid) are active to electrochemically catalyze oxygen evolution reaction (OER) in acid. The most active assembly, with Co loading at 3.31 × 10-8 mol cm-2, exhibits 374 mV onset overpotential and 497 mV overpotential to reach 1 mA cm-2 OER current in 0.1 M HClO4. The electron transfer rate constant (k) is acquired using Laviron's approach, and the results show that k is not affected by the carbon …

Dat3m: A Data Tracker For Multi-Faceted Management Of Multi-Site Clinical Research Data Submission, Curation, Master Inventorying, And Sharing, Shiqiang Tao, Licong Cui, Wei-Chun Chou, Samden Lhatoo, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Managing research data is an important and challenging aspect of clinical studies, especially for multi-site collaboratives. To address this challenge, we designed, developed and deployed a multi-faceted, multi-level interactive data tracker (DaT3M) for multi-site clinical research data submission, curation, master inventorying, and sharing. Components of DaT3M include data overview, data portal, data status panel, data query engine, and data downloader. DaT3M managed clinical research data for the Center for SUDEP Research (CSR). The CSR instance of DaT3M includes 2,743 subjects from seven data contributing institutions, 7 data modalities and 10,678 data components: 3,398 Epilepsy Monitoring Unit reports, 3,440 electroencephalography recordings, …

Deep Graph Convolutional Network For Us Birth Data Harmonization, Lishan Yu, Hamisu M Salihu, Deepa Dongarwar, Luyao Chen, Xiaoqian Jiang

Faculty, Staff and Student Publications

In this paper, we developed a feasible and efficient deep-learning-based framework to combine the United States (US) natality data for the last five decades, with changing variables and factors, into a consistent database. We constructed a graph based on the property and elements of databases, including variables, and conducted a graph convolutional network (GCN) to learn the embeddings of variables on the constructed graph, where the learned embeddings implied the similarity of variables. Specifically, we devised a loss function with a slack margin and a banlist mechanism (for a random walk) to learn the desired structure (two nodes sharing more …

Temporal Cohort Logic, Guo-Qiang Zhang, Xiaojin Li, Yan Huang, Licong Cui

Faculty, Staff and Student Publications

We introduce a new logic, called Temporal Cohort Logic (TCL), for cohort specification and discovery in clinical and population health research. TCL is created to fill a conceptual gap in formalizing temporal reasoning in biomedicine, in a similar role that temporal logics play for computer science and its applications. We provide formal syntax and semantics for TCL and illustrate the various logical constructs using examples related to human health. Relationships and distinctions with existing temporal logical frameworks are discussed. Applications in electronic health record (EHR) and in neurophysiological data resource are provided. Our approach differs from existing temporal logics, in …

Causal Inference Of Genetic Variants And Genes In Amyotrophic Lateral Sclerosis, Siyu Pan, Xinxuan Liu, Tianzi Liu, Zhongming Zhao, Yulin Dai, Yin-Ying Wang, Peilin Jia, Fan Liu

Faculty, Staff and Student Publications

Amyotrophic lateral sclerosis (ALS) is a fatal progressive multisystem disorder with limited therapeutic options. Although genome-wide association studies (GWASs) have revealed multiple ALS susceptibility loci, the exact identities of causal variants, genes, cell types, tissues, and their functional roles in the development of ALS remain largely unknown. Here, we reported a comprehensive post-GWAS analysis of the recent large ALS GWAS (n = 80,610), including functional mapping and annotation (FUMA), transcriptome-wide association study (TWAS), colocalization (COLOC), and summary data-based Mendelian randomization analyses (SMR) in extensive multi-omics datasets. Gene property analysis highlighted inhibitory neuron 6, oligodendrocytes, and GABAergic neurons (Gad1/Gad2) as …

Cross-Talk Between Histone Methyltransferases And Demethylases Regulate Rest Transcription During Neurogenesis, Jyothishmathi Swaminathan, Shinji Maegawa, Shavali Shaik, Ajay Sharma, Javiera Bravo-Alegria, Lei Guo, Lin Xu, Arif Harmanci, Vidya Gopalakrishnan

Faculty, Staff and Student Publications

The RE1 Silencing Transcription Factor (REST) is a major regulator of neurogenesis and brain development. Medulloblastoma (MB) is a pediatric brain cancer characterized by a blockade of neuronal specification. REST gene expression is aberrantly elevated in a subset of MBs that are driven by constitutive activation of sonic hedgehog (SHH) signaling in cerebellar granular progenitor cells (CGNPs), the cells of origin of this subgroup of tumors. To understand its transcriptional deregulation in MBs, we first studied control of Rest gene expression during neuronal differentiation of normal mouse CGNPs. Higher Rest expression was observed in proliferating CGNPs compared to differentiating neurons. …

Racial And Socioeconomic Disparities In Patients With Meningioma: A Retrospective Cohort Study, Hudin N Jackson, Caroline C Hadley, A Basit Khan, Ron Gadot, James C Bayley, Arya Shetty, Jacob Mandel, Ali Jalali, K Kelly Gallagher, Alex D Sweeney, Arif O Harmanci, Akdes S Harmanci, Tiemo Klisch, Shankar P Gopinath, Ganesh Rao, Daniel Yoshor, Akash J Patel

Faculty, Staff and Student Publications

BACKGROUND: Meningiomas are the most common intracranial neoplasms. Although genomic analysis has helped elucidate differences in survival, there is evidence that racial disparities may influence outcomes. African Americans have a higher incidence of meningiomas and poorer survival outcomes. The etiology of these disparities remains unclear, but may include a combination of pathophysiology and other factors.

OBJECTIVE: To determine factors that contribute to different clinical outcomes in racial populations.

METHODS: We retrospectively reviewed 305 patients who underwent resection for meningiomas at a single tertiary care facility. We used descriptive statistics and univariate, multivariable, and Kaplan-Meier analyses to study clinical, radiographical, and …

Risk Of Alzheimer's Disease Following Influenza Vaccination: A Claims-Based Cohort Study Using Propensity Score Matching, Avram S Bukhbinder, Yaobin Ling, Omar Hasan, Xiaoqian Jiang, Yejin Kim, Kamal N Phelps, Rosemarie E Schmandt, Albert Amran, Ryan Coburn, Srivathsan Ramesh, Qian Xiao, Paul E Schulz

Faculty, Staff and Student Publications

BACKGROUND: Prior studies have found a reduced risk of dementia of any etiology following influenza vaccination in selected populations, including veterans and patients with serious chronic health conditions. However, the effect of influenza vaccination on Alzheimer's disease (AD) risk in a general cohort of older US adults has not been characterized.

OBJECTIVE: To compare the risk of incident AD between patients with and without prior influenza vaccination in a large US claims database.

METHODS: Deidentified claims data spanning September 1, 2009 through August 31, 2019 were used. Eligible patients were free of dementia during the 6-year look-back period and≥65 years …

Fairly Predicting Graft Failure In Liver Transplant For Organ Assigning, Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse …

Facilitating Federated Genomic Data Analysis By Identifying Record Correlations While Ensuring Privacy, Leonard Dervishi, Xinyue Wang, Wentao Li, Anisa Halimi, Jaideep Vaidya, Xiaoqian Jiang, Erman Ayday

Faculty, Staff and Student Publications

With the reduction of sequencing costs and the pervasiveness of computing devices, genomic data collection is continually growing. However, data collection is highly fragmented and the data is still siloed across different repositories. Analyzing all of this data would be transformative for genomics research. However, the data is sensitive, and therefore cannot be easily centralized. Furthermore, there may be correlations in the data, which if not detected, can impact the analysis. In this paper, we take the first step towards identifying correlated records across multiple data repositories in a privacy-preserving manner. The proposed framework, based on random shuffling, synthetic record …

Caries Risk Documentation And Prevention: Emeasures For Dental Electronic Health Records, Suhasini Bangar, Ana Neumann, Joel M White, Alfa Yansane, Todd R Johnson, Gregory W Olson, Shwetha V Kumar, Krishna K Kookal, Aram Kim, Enihomo Obadan-Udoh, Elizabeth Mertz, Kristen Simmons, Joanna Mullins, Ryan Brandon, Muhammad F Walji, Elsbeth Kalenderian

Faculty, Staff and Student Publications

BACKGROUND: Longitudinal patient level data available in the electronic health record (EHR) allows for the development, implementation, and validations of dental quality measures (eMeasures).

OBJECTIVE: We report the feasibility and validity of implementing two eMeasures. The eMeasures determined the proportion of patients receiving a caries risk assessment (eCRA) and corresponding appropriate risk-based preventative treatments for patients at elevated risk of caries (appropriateness of care [eAoC]) in two academic institutions and one accountable care organization, in the 2019 reporting year.

METHODS: Both eMeasures define the numerator and denominator beginning at the patient level, populations' specifications, and validated the automated queries. For …

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, And Beyond, Robert El-Kareh, Dean F Sittig

Faculty, Staff and Student Publications

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.

Automated Identification Of Missing Is-A Relations In The Human Phenotype Ontology, Maryamsadat Mohtashamian, Ran Hu, Rashmie Abeysinghe, Xubing Hao, Hua Xu, Licong Cui

Faculty, Staff and Student Publications

Auditing the Human Phenotype Ontology (HPO) is necessary to provide accurate terminology for its use in clinical research. We investigate an approach leveraging the lexical features of concepts in HPO to identify missing IS-A relations among HPO concepts. We first model the names of HPO concepts as sets of words in lower case. Then, we generate two types of concept-pairs which have at least a single common word: (1) Linked concept-pairs generated from concept-pairs having an IS-A relation; (2) Unlinked concept-pairs generated from concept-pairs without an IS- A relation. Concept-pairs generate Derived Term Pairs (DTPs) emphasizing unique lexical information of …

Cost Of Care For Asylum Seekers And Refugees Entering The United States: The Case Of Volunteer Medical Providers In El Paso, Texas, Rigoberto I Delgado, Manuel De La Rosa, Marlon A Picado, Lisa Ayoub-Rodriguez, Celia E Gonzalez, Leopold Gemoets

Faculty, Staff and Student Publications

BACKGROUND: Between October 2018, and February 2020, the United States saw an unprecedented increase in the number of asylum seekers and refugees arriving unexpectedly at international crossings along the US-Mexico Border. Many of these migrants needed proper medical attention, and consequently created significant pressure on local health systems. In El Paso, Texas, volunteer clinicians, collaborating closely with religious organizations and non-governmental organizations, provided outpatient medical care for the new arrivals; the county hospital provided in-patient care at local tax payers' expense. The objective of this study was to estimate costs of healthcare services offered by these volunteers in order to …

A Nomogram For Predicting Upper Urinary Tract Damage Risk In Children With Neurogenic Bladder, Qi Li, Miao Cai, Qingsong Pu, Shengde Wu, Xing Liu, Tao Lin, Dawei He, Jianguo Wen, Guanghui Wei

Faculty, Staff and Student Publications

PURPOSE: To establish a predictive model for upper urinary tract damage (UUTD) in children with neurogenic bladder (NB) and verify its efficacy.

METHODS: A retrospective study was conducted that consisted of a training cohort with 167 NB patients and a validation cohort with 100 NB children. The clinical data of the two groups were compared first, and then univariate and multivariate logistic regression analyses were performed on the training cohort to identify predictors and develop the nomogram. The accuracy and clinical usefulness of the nomogram were verified by receiver operating characteristic (ROC) curve, calibration curve and decision curve analyses.

RESULTS: …

Phenotype-Genotype Analysis Of Caucasian Patients With High Risk Of Osteoarthritis, Yanfei Wang, Jacqueline Chyr, Pora Kim, Weiling Zhao, Xiaobo Zhou

Faculty, Staff and Student Publications

Background: Osteoarthritis (OA) is a common cause of disability and pain around the world. Epidemiologic studies of family history have revealed evidence of genetic influence on OA. Although many efforts have been devoted to exploring genetic biomarkers, the mechanism behind this complex disease remains unclear. The identified genetic risk variants only explain a small proportion of the disease phenotype. Traditional genome-wide association study (GWAS) focuses on radiographic evidence of OA and excludes sex chromosome information in the analysis. However, gender differences in OA are multifactorial, with a higher frequency in women, indicating that the chromosome X plays an essential role …

An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan-Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong-Wen Deng, Chaoyang Zhang

Faculty, Staff and Student Publications

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a …

Application Of Artificial Intelligence To Plasma Metabolomics Profiles To Predict Response To Neoadjuvant Chemotherapy In Triple-Negative Breast Cancer, Ehsan Irajizad, Ranran Wu, Jody Vykoukal, Eunice Murage, Rachelle Spencer, Jennifer B Dennison, Stacy Moulder, Elizabeth Ravenberg, Bora Lim, Jennifer Litton, Debu Tripathym, Vicente Valero, Senthil Damodaran, Gaiane M Rauch, Beatriz Adrada, Rosalind Candelaria, Jason B White, Abenaa Brewster, Banu Arun, James P Long, Kim Anh Do, Sam Hanash, Johannes F Fahrmann

Faculty, Staff and Student Publications

There is a need to identify biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are less likely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied …

Artificial Intelligence Algorithms For Medical Imaging And Healthcare, Jonathan William Stubblefield

Student Theses and Dissertations

In this dissertation, we studied several applications of artificial intelligence applications to healthcare. In the first chapter, we examined a machine learning algorithm for classifying patients presenting to the emergency department with acute respiratory distress syndrome (ARDS). Patients presenting with this life-threatening condition require a quick and accurate assessment of whether the condition is infectious or cardiac in etiology as the treatments for these etiologies of ARDS differ significantly. We used a transfer learning approach to develop our model. The model used a combination of clinical data and a chest x-ray as its input and achieved an accuracy 0.675 on …

Comprehensive Characterization Of Covid-19 Patients With Repeatedly Positive Sars-Cov-2 Tests Using A Large Us Electronic Health Record Database, Xiao Dong, Yujia Zhou, Xiao-Ou Shu, Elmer V Bernstam, Rebecca Stern, David M Aronoff, Hua Xu, Loren Lipworth

Faculty, Staff and Student Publications

In the absence of genome sequencing, two positive molecular tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible reinfection. Using a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results ≥60 days apart, separated by ≥2 consecutive negative test results. The prevalence of chronic medical conditions, symptoms, and severe outcomes related to coronavirus disease 19 (COVID-19) illness were ascertained. The median age of patients was 64.5 years, 40% were Black, and 39% …

Digital Technology Needs In Maternal Mental Health: A Qualitative Inquiry, Alexandra Zingg, Laura Carter, Deevakar Rogith, Amy Franklin, Sudhakar Selvaraj, Jerrie Refuerzo, Sahiti Myneni

Faculty, Staff and Student Publications

Digital technologies offer many opportunities to improve mental healthcare management for women seeking pre- and-postnatal care. They provide a discrete, practical medium that is well-suited for the sensitive nature of mental health. Women who are more prone to experiencing peripartum depression (PPD), such as those of low-socioeconomic background or in high-risk pregnancies, can benefit the most from such technologies. However, current digital interventions directed towards this population provide suboptimal support, and their responsiveness to end user needs is quite limited. Our objective is to understand the digital terrain of information needs for low-socioeconomic status women with high-risk pregnancies, specifically within …

Deep Learning Predicts Chromosomal Instability From Histopathology Images, Zhuoran Xu, Akanksha Verma, Uska Naveed, Samuel F. Bakhoum, Pegah Khosravi, Olivier Elemento

Publications and Research

Chromosomal instability (CIN) is a hallmark of human cancer yet not readily testable for patients with cancer in routine clinical setting. In this study, we sought to explore whether CIN status can be predicted using ubiquitously available hematoxylin and eosin histology through a deep learning-based model. When applied to a cohort of 1,010 patients with breast cancer (Training set: n = 858, Test set: n = 152) from The Cancer Genome Atlas where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve of 0.822 with 81.2% sensitivity and 68.7% specificity in …

Med-Bert: Pretrained Contextualized Embeddings On Large-Scale Structured Electronic Health Records For Disease Prediction, Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, Degui Zhi

Faculty, Staff and Student Publications

Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pretraining of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by BERT, we propose Med-BERT, which adapts the BERT framework originally developed …

Generalized And Transferable Patient Language Representation For Phenotyping With Limited Data, Yuqi Si, Elmer V Bernstam, Kirk Roberts

Faculty, Staff and Student Publications

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 …

A Deep Learning Approach To Diagnostic Classification Of Prostate Cancer Using Pathology–Radiology Fusion, Pegah Khosravi, Maria Lysandrou, Mahmoud Eljalby, Qianzi Li, Ehsan Kazemi, Pantelis Zisimopoulos, Alexandros Sigaras, Matthew Brendel, Josue Barnes, Camir Ricketts, Dmitry Meleshko, Andy Yat, Timothy D. Mcclure, Brian D. Robinson, Andrea Sboner, Olivier Elemento, Bilal Chughtai, Iman Hajirasouliha

Publications and Research

Background

A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications.

Purpose

To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information.

Study Type

Retrospective.

Population

Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases).

Field Strength/Sequence

1.5 to 3.0 Tesla, T2-weighted image pulse sequences.

Assessment

MR images reviewed and selected …

Exchanges In A Virtual Environment For Diabetes Self-Management Education And Support: Social Network Analysis, Carlos A Pérez-Aldana, Allison A Lewinski, Constance M Johnson, Allison A Vorderstrasse, Sahiti Myneni

Faculty, Staff and Student Publications

BACKGROUND: Diabetes remains a major health problem in the United States, affecting an estimated 10.5% of the population. Diabetes self-management interventions improve diabetes knowledge, self-management behaviors, and clinical outcomes. Widespread internet connectivity facilitates the use of eHealth interventions, which positively impacts knowledge, social support, and clinical and behavioral outcomes. In particular, diabetes interventions based on virtual environments have the potential to improve diabetes self-efficacy and support, while being highly feasible and usable. However, little is known about the patterns of social interactions and support taking place within type 2 diabetes-specific virtual communities.

OBJECTIVE: The objective of this study was to …

Knowledge Network Embedding Of Transcriptomic Data From Spaceflown Mice Uncovers Signs And Symptoms Associated With Terrestrial Diseases, Amber M. Paul, Charlotte A. Nelson, Ana Uriarte Acuna, Ryan T. Scott, Atul J. Butte, Egle Cekanaviciute, Sergio E. Baranzini

Publications

There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab …

A Comparison Of Exhaustive And Non-Lattice-Based Methods For Auditing Hierarchical Relations In Gene Ontology, Rashmie Abeysinghe, Fengbo Zheng, Licong Cui

Faculty, Staff and Student Publications

Uncovering and fixing errors in biomedical terminologies is essential so that they provide accurate knowledge to downstream applications that rely on them. Non-lattice-based methods have been applied to identify various kinds of inconsistencies in different biomedical terminologies. In previous work, we have introduced two inference-based approaches that were applied in an exhaustive manner to audit hierarchical relations in the Gene Ontology: (1) Lexical-based inference framework, and (2) Subsumption-based sub-term inference framework. However, it is unclear how effective these exhaustive approaches perform compared with their corresponding non-lattice-based approaches. Therefore, in this paper, we implement the non-lattice versions of these two exhaustive …