Evidence map›Paper›PMID 28925997›Full record

ArticleScientific data2017

Precision annotation of digital samples in NCBI's gene expression omnibus.

Dexter Hadley, James Pan, Osama El-Sayed, Jihad Aljabban, Imad Aljabban, Tej D Azad, Mohamad O Hadied, Shuaib Raza, Benjamin Abhishek Rayikanti, Bin Chen and 9 more

Abstract read
In one paragraph

Article in Scientific data, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 5 pooled it
–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

26 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  16. GeMI: interactive interface for transformer-based Genomic Metadata Integration.Database : the journal of biological databases and curation · 2022
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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

19 authors.

Dexter HadleyInstitute for Computational Health Sciences, University of California, San Francisco, California 94158, USA.
James PanDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, California 94305, USA.
Osama El-SayedUniversity of Illinois College of Medicine, Chicago, Illinois 60612, USA.
Jihad AljabbanHarvard Medical School Department of Immunology, Harvard University, Boston, Massachusetts 02115, USA.
Imad AljabbanHarvard Medical School Department of Immunology, Harvard University, Boston, Massachusetts 02115, USA.
Tej D AzadDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, California 94305, USA.
Mohamad O HadiedWayne State University School of Medicine, Detroit, Michigan 48201, USA.
Shuaib RazaYale School of Medicine, Yale University, New Haven, Connecticut 06519, USA.
Benjamin Abhishek RayikantiUniversity of Vermont Medical Center, University of Vermont, Burlington, Vermont 05401, USA.
Bin ChenInstitute for Computational Health Sciences, University of California, San Francisco, California 94158, USA.
Hyojung PaikInstitute for Computational Health Sciences, University of California, San Francisco, California 94158, USA.
Dvir AranInstitute for Computational Health Sciences, University of California, San Francisco, California 94158, USA.ORCID 0000-0001-6334-5039
Jordan SpatzInstitute for Computational Health Sciences, University of California, San Francisco, California 94158, USA.
Daniel HimmelsteinProgram in Biological &Medical Informatics, University of California, San Francisco, CA 94158, USA.ORCID 0000-0002-3012-7446
Maryam PanahiazarInstitute for Computational Health Sciences, University of California, San Francisco, California 94158, USA.
Sanchita BhattacharyaInstitute for Computational Health Sciences, University of California, San Francisco, California 94158, USA.ORCID 0000-0002-3056-0733
Marina SirotaInstitute for Computational Health Sciences, University of California, San Francisco, California 94158, USA.
Mark A MusenStanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California 94305, USA.
Atul J ButteInstitute for Computational Health Sciences, University of California, San Francisco, California 94158, USA.ORCID 0000-0002-7433-2740

Funding

RESOURCE-BASED CENTER FOR THE ADVANCEMENT OF PRECISION MEDICINE IN RHEUMATOLOGYP30AR070155 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Mary C Nakamura · 2016 to 2026
$8.2M
Repositioning drugs for the treatment of small cell lung cancerR01GM079719 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BUTTE, ATUL J · 2006 to 2015
$4.4M
Crowd-Assisted Deep Learning (CrADLe) Digital Curation to Translate Big Data into Precision MedicineU01LM012675 · NLM · UNIVERSITY OF CENTRAL FLORIDA · PI HADLEY, DEXTER D · 2017 to 2020
$2.1M
Crowd-sourcing A STAR Functional Genomic Characterization of Cancer with Open Big DataUH2CA203792 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HADLEY, DEXTER D · 2016 to 2017
$634k
NCI NIH HHS UH2 CA203792NIAID NIH HHS HHSN272201200028CNIAMS NIH HHS P30 AR070155NIGMS NIH HHS R01 GM079719NLM NIH HHS U01 LM012675
6 · The paper itself

Abstract

The Gene Expression Omnibus (GEO) contains more than two million digital samples from functional genomics experiments amassed over almost two decades. However, individual sample meta-data remains poorly described by unstructured free text attributes preventing its largescale reanalysis. We introduce the Search Tag Analyze Resource for GEO as a web application (http://STARGEO.org) to curate better annotations of sample phenotypes uniformly across different studies, and to use these sample annotations to define robust genomic signatures of disease pathology by meta-analysis. In this paper, we target a small group of biomedical graduate students to show rapid crowd-curation of precise sample annotations across all phenotypes, and we demonstrate the biological validity of these crowd-curated annotations for breast cancer. STARGEO.org makes GEO data findable, accessible, interoperable and reusable (i.e., FAIR) to ultimately facilitate knowledge discovery. Our work demonstrates the utility of crowd-curation and interpretation of open 'big data' under FAIR principles as a first step towards realizing an ideal paradigm of precision medicine.

Indexed as

Databases, GeneticData CurationGene ExpressionHumans

Identifiers

PMID28925997
PMCPMC5604135

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.