Evidence map›Paper›PMID 20851208›Full record

ArticleJournal of biomedical informatics2010

Exposing the cancer genome atlas as a SPARQL endpoint.

Helena F Deus, Diogo F Veiga, Pablo R Freire, John N Weinstein, Gordon B Mills, Jonas S Almeida

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. kpath: integration of metabolic pathway linked data.Database : the journal of biological databases and curation · 2015
    Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. MicroRNA 4423 is a primate-specific regulator of airway epithelial cell differentiation and lung carcinogenesis.Proceedings of the National Academy of Sciences of the United States of America · 2013
    Article
  9. A self-updating road map of The Cancer Genome Atlas.Bioinformatics (Oxford, England) · 2013
    Article
  10. Article
  11. Article
  12. Review
  13. 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

6 authors.

Helena F DeusDepartment of Bioinformatics and Computational Biology, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1410, Houston, TX 77230-1402, USA. mhdeus@mdanderson.org
Diogo F Veiga
Pablo R Freire
John N Weinstein
Gordon B Mills
Jonas S Almeida

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
UNIVERSITY OF TEXAS--SPORE IN LUNG CANCERP50CA070907 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI GIBBONS, DON LYNN · 1996 to 2024
$57.4M
Targeting the PI3K Signaling Pathway in Endometrial CarcinomaP50CA098258 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BODURKA, DIANE · 2003 to 2020
$32.0M
U OF TEXAS HLTH SCI CTR HOUSTON CENTER FOR CLINICAL AND TRANSLATIONAL SCIENCESUL1RR024148 · NCRR · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MCPHERSON, DAVID D · 2006 to 2010
$27.9M
Integrative Pipeline for Analysis & Translational Application of TCGA Data (GDAC)U24CA143883 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI AKBANI, REHAN, MILLS, GORDON B. · 2009 to 2016
$10.6M
NHLBI PROTEOMICS INITIATIVE-N01HV28181N01HV028181 · HV · MEDICAL UNIV OF SOUTH CAROLINA · PI KNAPP, DANIEL · 2002 to 2006
–
NCI NIH HHS P50 CA070907NCI NIH HHS P50 CA098258NCI NIH HHS P50 CA70907NCI NIH HHS U24 CA143883NCRR NIH HHS 1UL1RR024148NCRR NIH HHS UL1 RR024148NHLBI NIH HHS N01 HV028181NHLBI NIH HHS N01-HV-28181
6 · The paper itself

Abstract

The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to characterize several types of cancer. Datasets from biomedical domains such as TCGA present a particularly challenging task for those interested in dynamically aggregating its results because the data sources are typically both heterogeneous and distributed. The Linked Data best practices offer a solution to integrate and discover data with those characteristics, namely through exposure of data as Web services supporting SPARQL, the Resource Description Framework query language. Most SPARQL endpoints, however, cannot easily be queried by data experts. Furthermore, exposing experimental data as SPARQL endpoints remains a challenging task because, in most cases, data must first be converted to Resource Description Framework triples. In line with those requirements, we have developed an infrastructure to expose clinical, demographic and molecular data elements generated by TCGA as a SPARQL endpoint by assigning elements to entities of the Simple Sloppy Semantic Database (S3DB) management model. All components of the infrastructure are available as independent Representational State Transfer (REST) Web services to encourage reusability, and a simple interface was developed to automatically assemble SPARQL queries by navigating a representation of the TCGA domain. A key feature of the proposed solution that greatly facilitates assembly of SPARQL queries is the distinction between the TCGA domain descriptors and data elements. Furthermore, the use of the S3DB management model as a mediator enables queries to both public and protected data without the need for prior submission to a single data source.

Indexed as

Atlases as TopicGenomeComputational BiologyDatabases, FactualNeoplasmsSemantics

Identifiers

PMID20851208
PMCPMC3071752

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.