Article

Translational Bioinformatics: Linking Knowledge Across Biological and Clinical Realms

Center for Clinical and Translational Science, University of Vermont, Burlington, Vermont 05405, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.5). 07/2011; 18(4):354-7. DOI: 10.1136/amiajnl-2011-000245
Source: PubMed

ABSTRACT

Nearly a decade since the completion of the first draft of the human genome, the biomedical community is positioned to usher in a new era of scientific inquiry that links fundamental biological insights with clinical knowledge. Accordingly, holistic approaches are needed to develop and assess hypotheses that incorporate genotypic, phenotypic, and environmental knowledge. This perspective presents translational bioinformatics as a discipline that builds on the successes of bioinformatics and health informatics for the study of complex diseases. The early successes of translational bioinformatics are indicative of the potential to achieve the promise of the Human Genome Project for gaining deeper insights to the genetic underpinnings of disease and progress toward the development of a new generation of therapies.

Download full-text

Full-text

Available from: Peter Tarczy-Hornoch, Apr 14, 2015
    • "An external tool (GeneInsight, Alamut, or Cartagenia) can be integrated into the EHR to provide an alert at the time of electronic order entry[67]. The emerging discipline of translational bioinformatics focuses on such tools[68]. A challenge to putting actionable variants into the EHR is poor adherence to existing standards to represent genomic variants, such as the guideline proposed by the Human Genome Variation Society active genomic decision support (http://www.hgvs.org/mutnomen/recs/html) "

    No preview · Chapter · Jan 2016
  • Source
    • "As a result of over 30 years of research since its identification, CDH1/E-cadherin has been the subject of numerous studies that led to a vast number of reports in scientific journals (over 21,000 publications using " E-cadherin " keyword, 11,000 publications using " E-cadherin AND cancer " ; PubMed search on January 2015). This exceptional growth of information requires integrative approaches such as translational bioinformatics to transform the deluge of data into knowledge and, more importantly, to enable a deeper understanding of disease mechanisms and provide actionable information for the clinical practice (Altman, 2012; Sarkar et al., 2011). Publicly available comprehensive knowledge sources on disease genes are an important asset. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Cancer is a group of diseases that causes millions of deaths worldwide. Among cancers, Solid Tumors (ST) stand-out due to their high incidence and mortality rates. Disruption of cell-cell adhesion is highly relevant during tumor progression. Epithelial-cadherin (protein: E-cadherin, gene: CDH1) is a key molecule in cell-cell adhesion and an abnormal expression or/and function(s) contributes to tumor progression and is altered in ST. A systematic study was carried out to gather and summarize current knowledge on CDH1/E-cadherin and ST using bioinformatics resources. The DisGeNET database was exploited to survey CDH1-associated diseases. Reported mutations in specific ST were obtained by interrogating COSMIC and IntOGen tools. CDH1 Single Nucleotide Polymorphisms (SNP) were retrieved from the dbSNP database. DisGeNET analysis identified 609 genes annotated to ST, among which CDH1 was listed. Using CDH1 as query term, 26 disease concepts were found, 21 of which were neoplasms-related terms. Using DisGeNET ALL Databases, 172 disease concepts were identified. Of those, 80 ST disease-related terms were subjected to manual curation and 75/80 (93.75%) associations were validated. On selected ST, 489 CDH1 somatic mutations were listed in COSMIC and IntOGen databases. Breast neoplasms had the highest CDH1-mutation rate. CDH1 was positioned among the 20 genes with highest mutation frequency and was confirmed as driver gene in breast cancer. Over 14,000 SNP for CDH1 were found in the dbSNP database. This report used DisGeNET to gather/compile current knowledge on gene-disease association for CDH1/E-cadherin and ST; data curation expanded the number of terms that relate them. An updated list of CDH1 somatic mutations was obtained with COSMIC and IntOGen databases and of SNP from dbSNP. This information can be used to further understand the role of CDH1/E-cadherin in health and disease.
    Full-text · Article · Nov 2015 · Computational biology and chemistry
  • Source
    • "Infrastructure tools such as i2b2 and SHARPn have made searching, summarizing, and retrieving data from cohorts captured by the EHR more feasible [10] [11]. These developments have supported a growing body of work that utilize EHR data in identifying patient cohorts with specific diseases and conducting large population studies to mine associations between gene variants and clinical phenotypes [12] [13] [14]. Nevertheless, fulfilling the promise of precision medicine necessitates not only the ability to aggregate and mine information from multiple clinical data sources, but also novel approaches to obtain detailed characterizations of observations that provide sufficient context for studying the evolution of a patient's condition. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The electronic health record (EHR) contains a diverse set of clinical observations that are captured as part of routine care, but the incomplete, inconsistent, and sometimes incorrect nature of clinical data poses significant impediments for its secondary use in retrospective studies or comparative effectiveness research. In this work, we describe an ontology-driven approach for extracting and analyzing data from the patient record in a longitudinal and continuous manner. We demonstrate how the ontology helps enforce consistent data representation, integrates phenotypes generated through analyses of available clinical data sources, and facilitates subsequent studies to identify clinical predictors for an outcome of interest. Development and evaluation of our approach are described in the context of studying factors that influence intracranial aneurysm (ICA) growth and rupture. We report our experiences in capturing information on 78 individuals with a total of 120 aneurysms. Two example applications related to assessing the relationship between aneurysm size, growth, gene expression modules, and rupture are described. Our work highlights the challenges with respect to data quality, workflow, and analysis of data and its implications towards a learning health system paradigm. Copyright © 2015. Published by Elsevier Inc.
    Full-text · Article · Mar 2015 · Journal of Biomedical Informatics
Show more