ArticleBMC bioinformatics2009
Evaluation of a large-scale biomedical data annotation initiative.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
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Who cites it
7 citing papers in PubMed.
- MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.BMC bioinformatics · 2024Article
- AnyExpress: integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm.BMC bioinformatics · 2011Article
- Assessing the quality of annotations in asthma gene expression experiments.BMC bioinformatics · 2010Article
- DSGeo: software tools for cross-platform analysis of gene expression data in GEO.Journal of biomedical informatics · 2010Article
- Selected proceedings of the 2009 Summit on Translational Bioinformatics.BMC bioinformatics · 2009Article
- Towards large-scale sample annotation in gene expression repositories.BMC bioinformatics · 2009Article
- PhenDisco: phenotype discovery system for the database of genotypes and phenotypes.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundThis study describes a large-scale manual re-annotation of data samples in the Gene Expression Omnibus (GEO), using variables and values derived from the National Cancer Institute thesaurus. A framework is described for creating an annotation scheme for various diseases that is flexible, comprehensive, and scalable. The annotation structure is evaluated by measuring coverage and agreement between annotators.
resultsThere were 12,500 samples annotated with approximately 30 variables, in each of six disease categories - breast cancer, colon cancer, inflammatory bowel disease (IBD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and Type 1 diabetes mellitus (DM). The annotators provided excellent variable coverage, with known values for over 98% of three critical variables: disease state, tissue, and sample type. There was 89% strict inter-annotator agreement and 92% agreement when using semantic and partial similarity measures.
conclusionWe show that it is possible to perform manual re-annotation of a large repository in a reliable manner.
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Registered trials
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.