Evidence map›Paper›PMID 21044366›Full record

ArticleBMC bioinformatics2010

Assessing the quality of annotations in asthma gene expression experiments.

Ronilda Lacson, Michael Mbagwu, Hisham Yousif, Lucila Ohno-Machado

Abstract read
In one paragraph

Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Ronilda LacsonDecision Systems Group, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA. rlacson@dsg.harvard.edu
Michael Mbagwu
Hisham Yousif
Lucila Ohno-Machado

Funding

Assessing quality of individual predictions in medical decision support systemsR01LM009520 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI OHNO-MACHADO, LUCILA · 2007 to 2010
$1.4M
NLM NIH HHS R01LM009520
6 · The paper itself

Abstract

backgroundThe amount of data deposited in the Gene Expression Omnibus (GEO) has expanded significantly. It is important to ensure that these data are properly annotated with clinical data and descriptions of experimental conditions so that they can be useful for future analysis. This study assesses the adequacy of documented asthma markers in GEO. Three objective measures (coverage, consistency and association) were used for evaluation of annotations contained in 17 asthma studies.

resultsThere were 918 asthma samples with 20,640 annotated markers. Of these markers, only 10,419 had documented values (50% coverage). In one study carefully examined for consistency, there were discrepancies in drug name usage, with brand name and generic name used in different sections to refer to the same drug. Annotated markers showed adequate association with other relevant variables (i.e. the use of medication only when its corresponding disease state was present).

conclusionsThere is inadequate variable coverage within GEO and usage of terms lacks consistency. Association between relevant variables, however, was adequate.

Indexed as

Gene ExpressionAsthmaDatabases, GeneticGene Expression ProfilingHumansMolecular Sequence AnnotationResearch Design

Identifiers

PMID21044366
PMCPMC2967749

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.