We develop and benchmark semantic similarity measures over biomedical ontologies, including measures that operate on the OWL axiomatic structure of an ontology rather than only on its lexical or taxonomic skeleton. These measures underpin phenotype-based disease gene prioritization, ontology-aware protein function transfer, and biodiversity knowledge graph search.
- Section
- Methods (cross-cutting)
- Keywords
- semantic similarity, ontology similarity, Resnik, Lin, phenotype similarity, PhenomeNET similarity, OPA2Vec
Connections
related papers borg:linkedPaper
- Semantic similarity and machine learning with ontologies
- A Machine Learning Based Approach for Similarity Search on Biodiversity Knowledge Graphs
- Contribution of model organism phenotypes to the computational identification of human disease genes
- Notions of similarity for systems biology models
- Similarity-based search of model organism, disease and drug effect phenotypes
- Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases
- Evaluating the effect of annotation size on measures of semantic similarity
- OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
- Semantic prioritization of novel causative genomic variants
- Effects of Negation and Uncertainty Stratification on Text-Derived Patient Profile Similarity
- Evaluating semantic similarity methods for comparison of text-derived phenotype profiles
- PhenomeNET: a whole-phenome approach to disease gene discovery
- Quantitative comparison of mapping methods between Human and Mammalian Phenotype Ontology
- Evaluating the effect of annotation size on measures of semantic similarity
- Quantitative comparison of mapping methods between Human and Mammalian Phenotype Ontology
related projects borg:linkedProject
related people borg:linkedPerson
Referenced by
research topics borg:topic
- Towards sound, complete, and explainable machine learning with biomedical ontologies (CRG11)
- CompleX: Variant Prioritization in Complex Disease
- Bio2Vec: Smart analytics infrastructure for the life sciences
- Robert Hoehndorf
- Imane Boudellioua
- Mona Alshahrani
- Maxat Kulmanov
- Sarah Alghamdi
- Azza Althagafi
- Sumyyah Toonsi
- Fernando Zhapa-Camacho
- Abeer Almutairi
- Sara Althubaiti
- Hatoon Al Ali
- Safana Bakheet
- Mahdi Bu Ali
- Ashraf Kibraya
- Tengwei Song
- Paul N Schofield
- Georgios V Gkoutos
- Xin Gao
- Michel Dumontier
- Jens Lehmann
- Semantic similarity and machine learning with ontologies
- A Machine Learning Based Approach for Similarity Search on Biodiversity Knowledge Graphs
- Contribution of model organism phenotypes to the computational identification of human disease genes
- Notions of similarity for systems biology models
- Similarity-based search of model organism, disease and drug effect phenotypes
- Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases
- Evaluating the effect of annotation size on measures of semantic similarity
- OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
- Semantic prioritization of novel causative genomic variants
- Effects of Negation and Uncertainty Stratification on Text-Derived Patient Profile Similarity
- Evaluating semantic similarity methods for comparison of text-derived phenotype profiles
- PhenomeNET: a whole-phenome approach to disease gene discovery
- Quantitative comparison of mapping methods between Human and Mammalian Phenotype Ontology
- Evaluating the effect of annotation size on measures of semantic similarity
- Quantitative comparison of mapping methods between Human and Mammalian Phenotype Ontology
Open in the interactive graph →
JSON-LD (this resource)
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Machine-readable copy: data.jsonld. Full dataset: kg.jsonld.