ArticlePloS one2012
Mining GO annotations for improving annotation consistency.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Microarray-based characterization of airway inflammation induced by environmental Acanthamoeba exposure versus the ovalbumin-alum model.Parasites, hosts and diseases · 2025Article
- Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.Bioinformatics (Oxford, England) · 2024Article
- Exploring automatic inconsistency detection for literature-based gene ontology annotation.Bioinformatics (Oxford, England) · 2022Article
- GSAn: an alternative to enrichment analysis for annotating gene sets.NAR genomics and bioinformatics · 2020Article
- Optimizing gene set annotations combining GO structure and gene expression data.BMC systems biology · 2018Article
- A new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets.PloS one · 2018Article
- Positive and relaxed selection associated with flight evolution and loss in insect transcriptomes.GigaScience · 2017Article
- NoGOA: predicting noisy GO annotations using evidences and sparse representation.BMC bioinformatics · 2017Article
- An evidence-based approach to identify aging-related genes in Caenorhabditis elegans.BMC bioinformatics · 2015Article
- ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis.Nucleic acids research · 2015Article
- Optimization of gene set annotations via entropy minimization over variable clusters (EMVC).Bioinformatics (Oxford, England) · 2014Article
- Mining rare associations between biological ontologies.PloS one · 2014Article
- EcoGene 3.0.Nucleic acids research · 2013Article
- Article
- Biases in the experimental annotations of protein function and their effect on our understanding of protein function space.PLoS computational biology · 2013Article
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7 authors.
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Abstract
Despite the structure and objectivity provided by the Gene Ontology (GO), the annotation of proteins is a complex task that is subject to errors and inconsistencies. Electronically inferred annotations in particular are widely considered unreliable. However, given that manual curation of all GO annotations is unfeasible, it is imperative to improve the quality of electronically inferred annotations. In this work, we analyze the full GO molecular function annotation of UniProtKB proteins, and discuss some of the issues that affect their quality, focusing particularly on the lack of annotation consistency. Based on our analysis, we estimate that 64% of the UniProtKB proteins are incompletely annotated, and that inconsistent annotations affect 83% of the protein functions and at least 23% of the proteins. Additionally, we present and evaluate a data mining algorithm, based on the association rule learning methodology, for identifying implicit relationships between molecular function terms. The goal of this algorithm is to assist GO curators in updating GO and correcting and preventing inconsistent annotations. Our algorithm predicted 501 relationships with an estimated precision of 94%, whereas the basic association rule learning methodology predicted 12,352 relationships with a precision below 9%.
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