ArticleBMC bioinformatics2009
Comparison of concept recognizers for building the Open Biomedical Annotator.
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 75 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.
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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
75 citing papers in PubMed.
- Article
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- The effectiveness of large language models with RAG for auto-annotating trait and phenotype descriptions.Biology methods & protocols · 2025Article
- Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models.Journal of biomedical semantics · 2024Article
- Mapping Vaccine Names in Clinical Trials to Vaccine Ontology using Cascaded Fine-Tuned Domain-Specific Language Models.Research square · 2023Article
- Machine actionable metadata models.Scientific data · 2022Article
- Performance assessment of ontology matching systems for FAIR data.Journal of biomedical semantics · 2022Article
- Enhanced neurologic concept recognition using a named entity recognition model based on transformers.Frontiers in digital health · 2022Article
- An overview of graph databases and their applications in the biomedical domain.Database : the journal of biological databases and curation · 2021Article
- SIENA: Semi-automatic semantic enhancement of datasets using concept recognition.Journal of biomedical semantics · 2021Article
- Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review.Journal of medical Internet research · 2021Article
- Normalizing Adverse Events using Recurrent Neural Networks with Attention.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Automatic extraction and assessment of lifestyle exposures for Alzheimer's disease using natural language processing.International journal of medical informatics · 2019Article
- Integrating terminologies into standard SQL: a new approach for research on routine data.Journal of biomedical semantics · 2019Article
- Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT.Journal of digital imaging · 2019Article
- Integrating ontologies of human diseases, phenotypes, and radiological diagnosis.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- A Domain Knowledge-Enhanced LSTM-CRF Model for Disease Named Entity Recognition.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- MER: a shell script and annotation server for minimal named entity recognition and linking.Journal of cheminformatics · 2018Article
- SIFR annotator: ontology-based semantic annotation of French biomedical text and clinical notes.BMC bioinformatics · 2018Article
- Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The National Center for Biomedical Ontology (NCBO) is developing a system for automated, ontology-based access to online biomedical resources (Shah NH, et al.: Ontology-driven indexing of public datasets for translational bioinformatics. BMC Bioinformatics 2009, 10(Suppl 2):S1). The system's indexing workflow processes the text metadata of diverse resources such as datasets from GEO and ArrayExpress to annotate and index them with concepts from appropriate ontologies. This indexing requires the use of a concept-recognition tool to identify ontology concepts in the resource's textual metadata. In this paper, we present a comparison of two concept recognizers - NLM's MetaMap and the University of Michigan's Mgrep. We utilize a number of data sources and dictionaries to evaluate the concept recognizers in terms of precision, recall, speed of execution, scalability and customizability. Our evaluations demonstrate that Mgrep has a clear edge over MetaMap for large-scale service oriented applications. Based on our analysis we also suggest areas of potential improvements for Mgrep. We have subsequently used Mgrep to build the Open Biomedical Annotator service. The Annotator service has access to a large dictionary of biomedical terms derived from the United Medical Language System (UMLS) and NCBO ontologies. The Annotator also leverages the hierarchical structure of the ontologies and their mappings to expand annotations. The Annotator service is available to the community as a REST Web service for creating ontology-based annotations of their data.
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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.