Evidence map›Paper›PMID 26138794›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2015

The center for causal discovery of biomedical knowledge from big data.

Gregory F Cooper, Ivet Bahar, Michael J Becich, Panayiotis V Benos, Jeremy Berg, Jeremy U Espino, Clark Glymour, Rebecca Crowley Jacobson, Michelle Kienholz, Adrian V Lee and 3 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.medRxiv : the preprint server for health sciences · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Envisioning the future of 'big data' biomedicine.Journal of biomedical informatics · 2017
    Article
  14. Review
  15. Article
  16. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Gregory F CooperDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA gfc@pitt.edu.
Ivet BaharDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Michael J BecichDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Panayiotis V BenosDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Jeremy BergDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA Institute for Personalized Medicine, University of Pittsburgh and University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
Jeremy U EspinoDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Clark GlymourDepartment of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA.
Rebecca Crowley JacobsonDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Michelle KienholzInstitute for Personalized Medicine, University of Pittsburgh and University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
Adrian V LeeDepartment of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Xinghua LuDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Richard ScheinesDietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
Center for Causal Discovery team

Funding

TrainingU54HG008540 · NHGRI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BAHAR, IVET, BERG, JEREMY M · 2014 to 2018
$12.8M
Interpretable deep learning models for translational medicine RenewalR01LM012011 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Tanner J. Freeman, XINGHUA LU · 2015 to 2026
$3.6M
NHGRI NIH HHS U54 HG008540NHGRI NIH HHS U54HG008540NLM NIH HHS R01 LM012011
6 · The paper itself

Abstract

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.

Indexed as

AlgorithmsDatasets as TopicTranslational Research, BiomedicalBiomedical ResearchHumansUnited StatesBig Data to knowledge (BD2K)biomedical knowledgebiomedical sciencecausal discoverycenter of excellence

Identifiers

PMID26138794
PMCPMC5009908

What OpenQuestion holds

Textmetadata
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.