ArticleScientific reports2018
GOGO: An improved algorithm to measure the semantic similarity between gene ontology terms.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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59 citing papers in PubMed, 125 citations in OpenAlex.
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- Pathway Analysis Interpretation in the Multi-Omic Era.Biotech (Basel (Switzerland)) · 2025Review
- Evaluating sequence and structural similarity metrics for predicting shared paralog functions.NAR genomics and bioinformatics · 2025Article
- Protein function prediction using GO similarity-based heterogeneous network propagation.Scientific reports · 2025Article
- Expanding kinetoplastid genome annotation through protein structure comparison.PLoS pathogens · 2025Article
- Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.Bioinformatics (Oxford, England) · 2025Article
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- Genome-wide association analyses identify distinct genetic architectures for age-related macular degeneration across ancestries.Nature genetics · 2024Article
- simona: a comprehensive R package for semantic similarity analysis on bio-ontologies.BMC genomics · 2024Article
- Interpreting and visualizing pathway analyses using embedding representations with PAVER.Bioinformation · 2024Article
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- Learning sequence, structure, and function representations of proteins with language models.bioRxiv : the preprint server for biology · 2023Article
- Broad functional profiling of fission yeast proteins using phenomics and machine learning.eLife · 2023Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Measuring the semantic similarity between Gene Ontology (GO) terms is an essential step in functional bioinformatics research. We implemented a software named GOGO for calculating the semantic similarity between GO terms. GOGO has the advantages of both information-content-based and hybrid methods, such as Resnik's and Wang's methods. Moreover, GOGO is relatively fast and does not need to calculate information content (IC) from a large gene annotation corpus but still has the advantage of using IC. This is achieved by considering the number of children nodes in the GO directed acyclic graphs when calculating the semantic contribution of an ancestor node giving to its descendent nodes. GOGO can calculate functional similarities between genes and then cluster genes based on their functional similarities. Evaluations performed on multiple pathways retrieved from the saccharomyces genome database (SGD) show that GOGO can accurately and robustly cluster genes based on functional similarities. We release GOGO as a web server and also as a stand-alone tool, which allows convenient execution of the tool for a small number of GO terms or integration of the tool into bioinformatics pipelines for large-scale calculations. GOGO can be freely accessed or downloaded from http://dna.cs.miami.edu/GOGO/ .
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