ArticleJournal of biomedical semantics2013
A task-based approach for Gene Ontology evaluation.
Article in Journal of biomedical semantics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A review on Gene Ontology evaluations.Database : the journal of biological databases and curation · 2025Review
- BioHackathon 2015: Semantics of data for life sciences and reproducible research.F1000Research · 2020Article
- Monitoring changes in the Gene Ontology and their impact on genomic data analysis.GigaScience · 2018Article
- Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations.Scientific reports · 2018Article
- NoGOA: predicting noisy GO annotations using evidences and sparse representation.BMC bioinformatics · 2017Article
- Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement.Journal of biomedical informatics · 2015Article
- Pitfalls in the application of gene-set analysis to genetics studies.Trends in genetics : TIG · 2014Article
- Evaluating the Emotion Ontology through use in the self-reporting of emotional responses at an academic conference.Journal of biomedical semantics · 2014Article
- Selected papers from the 15th Annual Bio-Ontologies Special Interest Group Meeting.Journal of biomedical semantics · 2013Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe Gene Ontology and its associated annotations are critical tools for interpreting lists of genes. Here, we introduce a method for evaluating the Gene Ontology annotations and structure based on the impact they have on gene set enrichment analysis, along with an example implementation. This task-based approach yields quantitative assessments grounded in experimental data and anchored tightly to the primary use of the annotations.
resultsApplied to specific areas of biological interest, our framework allowed us to understand the progress of annotation and structural ontology changes from 2004 to 2012. Our framework was also able to determine that the quality of annotations and structure in the area under test have been improving in their ability to recall underlying biological traits. Furthermore, we were able to distinguish between the impact of changes to the annotation sets and ontology structure.
conclusionOur framework and implementation lay the groundwork for a powerful tool in evaluating the usefulness of the Gene Ontology. We demonstrate both the flexibility and the power of this approach in evaluating the current and past state of the Gene Ontology as well as its applicability in developing new methods for creating gene annotations.
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