A review on Gene Ontology evaluations

The analysis of the large and heterogeneous datasets that characterize modern biology demands systems capable of representing biological knowledge in a formal, standardized manner. The most widely used structured vocabulary in molecular biology and biomedicine is the Gene Ontology (GO). Although employed in many different data analysis tasks, its main application is in the functional annotation of gene products and the subsequent functional enrichment analysis of large gene/protein datasets derived from omics experiments. This key position in modern biology means the GO is subject to continuous scrutiny and improvement, not only by its developers but by the community in general. GO's vocabulary, structure, and gene annotations have been evaluated from multiple points of view, revealing important insights into how we organize our biological knowledge, the associated problems, and strategies for overcoming them. These evaluations range from technical analyses of its ontological architecture to assessments of its effectiveness in supporting biological research. This process ensures that this essential resource is updated and adapted to the new challenges posed by modern biology. In this review, we summarize some key evaluations that the GO has undergone over time.

Authors

Institutions

Publication Details

Journal
Database
Published
2025-01-01
DOI
https://doi.org/10.1093/database/baaf058
Citations
3
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
2.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A review on Gene Ontology evaluations

Mónica Chagoyen, Borja Pitarch, Florencio Pazos, Juan A. G. Ranea
3 citations
Database
Biomedical Text Mining and Ontologies
2.42
article

A review on Gene Ontology evaluations

Mónica Chagoyen, Borja Pitarch, Florencio Pazos, Juan A. G. Ranea
article en
3 citations

Abstract

The analysis of the large and heterogeneous datasets that characterize modern biology demands systems capable of representing biological knowledge in a formal, standardized manner. The most widely used structured vocabulary in molecular biology and biomedicine is the Gene Ontology (GO). Although employed in many different data analysis tasks, its main application is in the functional annotation of gene products and the subsequent functional enrichment analysis of large gene/protein datasets derived from omics experiments. This key position in modern biology means the GO is subject to continuous scrutiny and improvement, not only by its developers but by the community in general. GO's vocabulary, structure, and gene annotations have been evaluated from multiple points of view, revealing important insights into how we organize our biological knowledge, the associated problems, and strategies for overcoming them. These evaluations range from technical analyses of its ontological architecture to assessments of its effectiveness in supporting biological research. This process ensures that this essential resource is updated and adapted to the new challenges posed by modern biology. In this review, we summarize some key evaluations that the GO has undergone over time.

DatabaseVol. 2025
Centro Nacional de Biotecnología (ES), Andalusian Centre for Nanomedicine and Biotechnology (ES), Instituto de Investigación de Enfermedades Raras (ES), Elixir Medical (United States) (US), Universidad de Málaga (ES)
Ministerio de Economía y Competitividad, European Regional Development Fund
Openalex Percentile: Top 10%
Biomedical Text Mining and Ontologies
2.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.