Evidence map›Paper›PMID 17921495›Full record

ArticleBioinformatics (Oxford, England)2007

Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes.

Sangeeta B English, Atul J Butte

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in Bioinformatics (Oxford, England), 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 2 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

31 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  8. In silico methods for drug repurposing and pharmacology.Wiley interdisciplinary reviews. Systems biology and medicine · 2016
    Review
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  15. Identifying common genes and networks in multi-organ fibrosis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2012
    Article
  16. An automated bayesian framework for integrative gene expression analysis and predictive medicine.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2012
    Article
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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

2 authors.

Sangeeta B EnglishDepartment of Medicine, Stanford Medical Informatics, Stanford University School of Medicine, Lucile Packard Children's Hospital, Stanford, CA 94305, USA.
Atul J Butte

Funding

Diabetes Genome Anatomy Project (DGAP)R01DK060837 · NIDDK · JOSLIN DIABETES CENTER · PI KAHN, C RONALD · 2002 to 2006
$15.9M
Career Development in Diabetes for Pediatric EndocrinoloK12DK063696 · NIDDK · JOSLIN DIABETES CENTER · PI LAFFEL, LORI M · 2002 to 2007
$2.8M
Gene Expression in Prediabetes: Potential Role of PGC-1R01DK062948 · NIDDK · JOSLIN DIABETES CENTER · PI PATTI, MARY E · 2003 to 2006
$1.4M
CREATION AND APPLICATION OF A DIABETES KNOWLEDGE BASEK22LM008261 · NLM · STANFORD UNIVERSITY · PI BUTTE, ATUL J. · 2005 to 2007
$460k
NIDDK NIH HHS K12 DK063696NIDDK NIH HHS K12 DK63696NIDDK NIH HHS R01 DK060837NIDDK NIH HHS R01 DK062948NIDDK NIH HHS R01 DK62948NLM NIH HHS K22 LM008261
6 · The paper itself

Abstract

motivationGenome-wide experiments only rarely show resounding success in yielding genes associated with complex polygenic disorders. We evaluate 49 obesity-related genome-wide experiments with publicly available findings including microarray, genetics, proteomics and gene knock-down from human, mouse, rat and worm, in terms of their ability to rediscover a comprehensive set of genes previously found to be causally associated or having variants associated with obesity.

resultsIndividual experiments show poor predictive ability for rediscovering known obesity-associated genes. We show that intersecting the results of experiments significantly improves the sensitivity, specificity and precision of the prediction of obesity-associated genes. We create an integrative model that statistically significantly outperforms all 49 individual genome-wide experiments. We find that genes known to be associated with obesity are significantly implicated in more obesity-related experiments and use this to provide a list of genes that we predict to have the highest likelihood of association for obesity. The approach described here can include any number and type of genome-wide experiments and might be useful for other complex polygenic disorders as well.

Indexed as

AlgorithmsDatabases, ProteinBiomarkersChromosome MappingGene Expression ProfilingHumansInformation Storage and RetrievalObesityProteomeReproducibility of ResultsSensitivity and SpecificitySystems IntegrationBiomarkersProteome

Identifiers

PMID17921495
PMCPMC2839901

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.