Evidence map›Paper›PMID 21624155›Full record

ArticleJournal of biomedical semantics2011

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.

Joanne S Luciano, Bosse Andersson, Colin Batchelor, Olivier Bodenreider, Tim Clark, Christine K Denney, Christopher Domarew, Thomas Gambet, Lee Harland, Anja Jentzsch and 18 more

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Review
  2. Text Snippets to Corroborate Medical Relations: An Unsupervised Approach using a Knowledge Graph and Embeddings.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020
    Article
  3. UMLS to DBPedia link discovery through circular resolution.Journal of the American Medical Informatics Association : JAMIA · 2018
    Article
  4. Article
  5. A path to precision in the ICU.Critical care (London, England) · 2017
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Mining Electronic Health Records using Linked Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Extending the "web of drug identity" with knowledge extracted from United States product labels.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2013
    Article
  19. The future state of clinical data capture and documentation: a report from AMIA's 2011 Policy Meeting.Journal of the American Medical Informatics Association : JAMIA · 2013
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

28 authors.

Joanne S LucianoRensselaer Polytechnic Institute, Troy, NY, USA. jluciano@cs.rpi.edu.
Bosse Andersson
Colin Batchelor
Olivier Bodenreider
Tim Clark
Christine K Denney
Christopher Domarew
Thomas Gambet
Lee Harland
Anja Jentzsch
Vipul Kashyap
Peter Kos
Julia Kozlovsky
Timothy Lebo
Scott M Marshall
Jamie P. McCusker
Deborah L McGuinness
Chimezie Ogbuji
Elgar Pichler
Robert L Powers
Eric Prud'hommeaux
Matthias Samwald
Lynn Schriml
Peter J Tonellato
Patricia L Whetzel
Jun Zhao
Susie Stephens
Michel Dumontier

Funding

PREDICTIVE OPTIMAL ANTICLOTTING TREATMENT FOR SEGMENTED PATIENT POPULATIONSR01LM011566 · NLM · UNIVERSITY OF MISSOURI-COLUMBIA · PI TONELLATO, PETER J. · 2013 to 2016
$1.0M
Method for Prediction of Efficacy of Genetic-Based Prediction Models of PersonaliR01LM010130 · NLM · HARVARD MEDICAL SCHOOL · PI TONELLATO, PETER J. · 2009 to 2010
$967k
Intramural NIH HHS Z99 LM999999NLM NIH HHS R01 LM010130NLM NIH HHS R01 LM011566
6 · The paper itself

Abstract

backgroundTranslational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery.

resultsWe developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action.

conclusionsThis work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine. AVAILABILITY: TMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql.

Identifiers

PMID21624155
PMCPMC3102889

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.