The Open Biomedical Annotator

By C. Jonquet, Nigam H. Shah, Mark A. Musen. Summit on translational bioinformatics, 2009

306 citations recorded by the source corpus.

The range of publicly available biomedical data is enormous and is expanding fast. This expansion means that researchers now face a hurdle to extracting the data they need from the large numbers of data that are available. Biomedical researchers have turned to ontologies and terminologies to structure and annotate their data with ontology concepts for better search and retrieval. However, this annotation process cannot be easily automated and often requires expert curators. Plus, there is a lack of easy-to-use systems that facilitate the use of ontologies for annotation. This paper presents the Open Biomedical Annotator (OBA), an ontology-based Web service that annotates public datasets with biomedical ontology concepts based on their textual metadata (www.bioontology.org). The biomedical community can use the annotator service to tag datasets automatically with ontology terms (from UMLS and NCBO BioPortal ontologies). Such annotations facilitate translational discoveries by integrating annotated data.[1]

View Paper: https://www.semanticscholar.org/paper/e7524611e3b3b87bf8aef24ee66ad833c7b0333b

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Citations in the corpus

MyMiner: a web application for computer-assisted biocuration and text annotation (2012, unknown): “, 2012), Knowtator (Ogren, 2006), NCBO Annotator (Jonquet et al., 2008), DOMEO/SWAN (Ciccarese et al.”
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records (2016, background): “…Biomedical Annotator and its RESTful API leverages the National Center for Biomedical Ontology (NCBO) BioPortal 20 , which provides a large set of ontologies, including SNOMED-CT, UMLS and RxNorm, to extract biomedical concepts from text and to provide their normalized and standard versions 21 .”
Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. (2013, background): “We ﬁ rst identi ﬁ ed those concepts that were (a) signi ﬁ cantly enriched among patients with a particular mental disorder relative to the general population and (b) not indicative of the patient population at a particular facility, but rather, indicative of the disorder itself.”
Clinical Natural Language Processing in languages other than English: opportunities and challenges (2018, unknown): “MetaMap [133], cTAKES [134, 135], NCBO [136]).”