ArticleJournal of biomedical semantics2017
Integrating phenotype ontologies with PhenomeNET.
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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Who cites it
14 citing papers in PubMed.
- The Unified Phenotype Ontology : a framework for cross-species integrative phenomics.Genetics · 2025Article
- The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics.bioRxiv : the preprint server for biology · 2024Article
- Linking common human diseases to their phenotypes; development of a resource for human phenomics.Journal of biomedical semantics · 2021Article
- Predicting candidate genes from phenotypes, functions and anatomical site of expression.Bioinformatics (Oxford, England) · 2021Article
- Formal axioms in biomedical ontologies improve analysis and interpretation of associated data.Bioinformatics (Oxford, England) · 2020Article
- DeepPVP: phenotype-based prioritization of causative variants using deep learning.BMC bioinformatics · 2019Article
- Computational aspects underlying genome to phenome analysis in plants.The Plant journal : for cell and molecular biology · 2019Review
- Ontology based text mining of gene-phenotype associations: application to candidate gene prediction.Database : the journal of biological databases and curation · 2019Article
- OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants.Scientific reports · 2018Article
- Model organism data evolving in support of translational medicine.Lab animal · 2018Review
- Ontology-based validation and identification of regulatory phenotypes.Bioinformatics (Oxford, England) · 2018Article
- Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes.Bioinformatics (Oxford, England) · 2018Article
- Biomedical ontology alignment: an approach based on representation learning.Journal of biomedical semantics · 2018Article
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4 authors.
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Abstract
backgroundIntegration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast.
resultsHere, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies.
conclusionsPhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.
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