ArticleBMC bioinformatics2014
Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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61 citing papers in PubMed.
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- Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.Bioinformatics (Oxford, England) · 2024Article
- Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: A use case studying depression as a risk factor for Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Classifying literature mentions of biological pathogens as experimentally studied using natural language processing.Journal of biomedical semantics · 2023Article
- A Gated Recurrent Unit based architecture for recognizing ontology concepts from biological literature.BioData mining · 2022Article
- Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research.Journal of biomedical semantics · 2022Article
- Exploring automatic inconsistency detection for literature-based gene ontology annotation.Bioinformatics (Oxford, England) · 2022Article
- COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments.Pharmaceutics · 2022Article
- Enhanced neurologic concept recognition using a named entity recognition model based on transformers.Frontiers in digital health · 2022Article
- Automatic consistency assurance for literature-based gene ontology annotation.BMC bioinformatics · 2021Article
- Context-aware multi-token concept recognition of biological entities.BMC bioinformatics · 2021Article
- [Making COVID-19 research data more accessible-building a nationwide information infrastructure].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2021Review
- Visualizing the phenotype diversity: a case study of Alexander disease.Genomics & informatics · 2021Article
- Towards similarity-based differential diagnostics for common diseases.Computers in biology and medicine · 2021Article
- Improved characterisation of clinical text through ontology-based vocabulary expansion.Journal of biomedical semantics · 2021Article
- Ontology-driven integrative analysis of omics data through Onassis.Scientific reports · 2020Article
- Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model.Frontiers in human neuroscience · 2020Article
- Open Agile text mining for bioinformatics: the PubAnnotation ecosystem.Bioinformatics (Oxford, England) · 2019Article
1 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
backgroundOntological concepts are useful for many different biomedical tasks. Concepts are difficult to recognize in text due to a disconnect between what is captured in an ontology and how the concepts are expressed in text. There are many recognizers for specific ontologies, but a general approach for concept recognition is an open problem.
resultsThree dictionary-based systems (MetaMap, NCBO Annotator, and ConceptMapper) are evaluated on eight biomedical ontologies in the Colorado Richly Annotated Full-Text (CRAFT) Corpus. Over 1,000 parameter combinations are examined, and best-performing parameters for each system-ontology pair are presented.
conclusionsBaselines for concept recognition by three systems on eight biomedical ontologies are established (F-measures range from 0.14-0.83). Out of the three systems we tested, ConceptMapper is generally the best-performing system; it produces the highest F-measure of seven out of eight ontologies. Default parameters are not ideal for most systems on most ontologies; by changing parameters F-measure can be increased by up to 0.4. Not only are best performing parameters presented, but suggestions for choosing the best parameters based on ontology characteristics are presented.
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