ArticleJournal of biomedical semantics2017
Evaluating the effect of annotation size on measures of semantic similarity.
Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients.Scientific reports · 2025Article
- Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning.Bioinformatics (Oxford, England) · 2024Article
- Contribution of model organism phenotypes to the computational identification of human disease genes.Disease models & mechanisms · 2022Article
- Evaluating semantic similarity methods for comparison of text-derived phenotype profiles.BMC medical informatics and decision making · 2022Article
- Multi-faceted semantic clustering with text-derived phenotypes.Computers in biology and medicine · 2021Article
- Semantic similarity and machine learning with ontologies.Briefings in bioinformatics · 2021Review
- UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization.PloS one · 2020Article
- Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies.Scientific reports · 2019Article
- Ontology based text mining of gene-phenotype associations: application to candidate gene prediction.Database : the journal of biological databases and curation · 2019Article
- As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018.Yearbook of medical informatics · 2018Review
- Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.Bioinformatics (Oxford, England) · 2018Article
- PhenoRank: reducing study bias in gene prioritization through simulation.Bioinformatics (Oxford, England) · 2018Article
- Article
- Improved ontology-based similarity calculations using a study-wise annotation model.Database : the journal of biological databases and curation · 2018Article
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2 authors.
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Abstract
backgroundOntologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are, and semantic similarity is increasingly applied in applications ranging from diagnosis of disease to investigation in gene networks and functions of gene products.
resultsHere, we analyze a large number of semantic similarity measures and the sensitivity of similarity values to the number of annotations of entities, difference in annotation size and to the depth or specificity of annotation classes. We find that most similarity measures are sensitive to the number of annotations of entities, difference in annotation size as well as to the depth of annotation classes; well-studied and richly annotated entities will usually show higher similarity than entities with only few annotations even in the absence of any biological relation.
conclusionsOur findings may have significant impact on the interpretation of results that rely on measures of semantic similarity, and we demonstrate how the sensitivity to annotation size can lead to a bias when using semantic similarity to predict protein-protein interactions.
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