ArticlePLoS computational biology2012
Predicting the extension of biomedical ontologies.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation.Database : the journal of biological databases and curation · 2019Article
- Analyzing user interactions with biomedical ontologies: A visual perspective.Web semantics (Online) · 2018Article
- Evolution of biomedical ontologies and mappings: Overview of recent approaches.Computational and structural biotechnology journal · 2016Review
- Management of Dynamic Biomedical Terminologies: Current Status and Future Challenges.Yearbook of medical informatics · 2015Review
- Analysis and Prediction of User Editing Patterns in Ontology Development Projects.Journal on data semantics · 2015Article
- Discovering beaten paths in collaborative ontology-engineering projects using Markov chains.Journal of biomedical informatics · 2014Article
- Annotation extension through protein family annotation coherence metrics.Frontiers in genetics · 2013Article
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Authors and funding
2 authors.
Funding
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Abstract
Developing and extending a biomedical ontology is a very demanding task that can never be considered complete given our ever-evolving understanding of the life sciences. Extension in particular can benefit from the automation of some of its steps, thus releasing experts to focus on harder tasks. Here we present a strategy to support the automation of change capturing within ontology extension where the need for new concepts or relations is identified. Our strategy is based on predicting areas of an ontology that will undergo extension in a future version by applying supervised learning over features of previous ontology versions. We used the Gene Ontology as our test bed and obtained encouraging results with average f-measure reaching 0.79 for a subset of biological process terms. Our strategy was also able to outperform state of the art change capturing methods. In addition we have identified several issues concerning prediction of ontology evolution, and have delineated a general framework for ontology extension prediction. Our strategy can be applied to any biomedical ontology with versioning, to help focus either manual or semi-automated extension methods on areas of the ontology that need extension.
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