Evaluation of a Large-Scale Biomedical Data Annotation Initiative
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CitationLacson, Ronilda, Erik Pitzer, Christian Hinske, Pedro Galante, and Lucila Ohno-Machado. 2009. Evaluation of a large-scale biomedical data annotation initiative. BMC Bioinformatics 10(Suppl 9): S10.
AbstractBackground: This 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. Results: There 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. Conclusion: We show that it is possible to perform manual re-annotation of a large repository in a reliable manner.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4931095
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