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Comparison of concept recognizers for building the Open Biomedical Annotator
Nigam H Shah1, Nipun Bhatia, Clement Jonquet
1Centre for Biomedical Informatics, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu
BMC Bioinformatics
|September 19, 2009
Summary
Mgrep outperforms MetaMap for large-scale biomedical data annotation, enabling the Open Biomedical Annotator service for enhanced data access and translational bioinformatics.
Area of Science:
- Biomedical Informatics
- Translational Bioinformatics
- Computational Biology
Background:
- The National Center for Biomedical Ontology (NCBO) is developing automated systems for accessing online biomedical resources.
- Ontology-driven indexing is crucial for annotating and indexing diverse resources like GEO and ArrayExpress datasets.
- Concept recognition tools are essential for identifying ontology concepts within textual metadata.
Purpose of the Study:
- To compare the performance of two concept recognizers, NLM's MetaMap and the University of Michigan's Mgrep.
- To evaluate these tools based on precision, recall, execution speed, scalability, and customizability.
- To determine the most suitable tool for large-scale, service-oriented applications in biomedical informatics.
Main Methods:
- Utilized multiple data sources and dictionaries for comprehensive evaluation.
- Assessed concept recognizers on key performance metrics including precision, recall, speed, scalability, and customizability.
- Benchmarked NLM's MetaMap against the University of Michigan's Mgrep.
Main Results:
- Mgrep demonstrated a significant advantage over MetaMap for large-scale, service-oriented applications.
- Identified specific areas for potential improvement within the Mgrep tool.
- Mgrep was subsequently used to develop the Open Biomedical Annotator service.
Conclusions:
- Mgrep is a superior choice for large-scale ontology-based annotation in biomedical informatics.
- The Open Biomedical Annotator service, powered by Mgrep, provides enhanced access to biomedical data.
- This service leverages UMLS and NCBO ontologies for comprehensive and expanded annotations via a REST Web service.
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