The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease.

By Peter N. Robinson, Sebastian Köhler, Sebastian Bauer, D. Seelow, D. Horn, Stefan Mundlos. American Journal of Human Genetics, 2008

1,006 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c7115e502f180548b03e5745cb398be1eee7ebca

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Citations in the corpus

Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation (2009, background): “Furthermore, coordinating these standardized terminologies with the development of the Human Phenotype Ontology (HPO) [49] and in creating OMIM clinical synopses will be a necessity.”
Computational tools for prioritizing candidate genes: boosting disease gene discovery (2012, background): “Relevant examples here include Gene Ontology 12 , Human Phenotype Ontology 123 or Disease Ontology 124 .”
ClinVar: public archive of relationships among sequence variation and human phenotype (2013, background): “Submitters are encouraged to submit phenotypic information via identifier, e.g. MIM number, MeSH term, or identifier from the Human Phenotype Ontology (HPO) (10).”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, methodology): “It is recommended to use systematics in variation databases whenever possible, including those for phenotype such as the Human Phenotype Ontology (HPO) [Robinson et al., 2008] and the Variation Ontology (VariO) [Vihinen, 2014b] for a systematic description of effects, mechanisms, and consequences of”
Why digital medicine depends on interoperability (2019, background): “It can be complemented by more domain-specific terminologies such as, for example, Logical Observation Identifiers Names and Codes (LOINC) for laboratory observations,(22) the Identification of Medicinal Products (IDMP) for medicines,(23) the nomenclature of the HUGO Gene Nomenclature Committee (HGN”
Massive mining of publicly available RNA-seq data from human and mouse (2017, background): “These single gene landing pages contain predicted biological functions based on correlations with genes assigned to GO categories; predicted upstream transcription factors based on correlation with identified putative targets from ChEA and ENCODE; predicted knockout mouse phenotypes based on annotat”
The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data (2013, background): “The Human Phenotype Ontology (HPO) project (6) was initiated in 2007 to enable sophisticated integration of phenotype information across scientiﬁc ﬁelds and databases.”
The Mammalian Phenotype Ontology: enabling robust annotation and comparative analysis (2009, background): “Phenotypic comparisons to data annotated with other precoordinated phenotype ontologies, such as the recently described Human Phenotype Ontology (HPO) 19 can be made if such logical deﬁnitions are provided, or by storing term mappings to the HPO directly.”
Network-based methods for human disease gene prediction. (2011, neutral): “Other phenotype similarity measures were also reported based on reciprocal references or the constructed human phenotype ontology [51, 52].”
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources (2018, positive): “The HPO annotation file format had remained unchanged since the first publication of the HPO in 2008 (57); to accommodate the aforementioned new annotation resources, we have updated the annotation file format.”
Integrating phenotype ontologies across multiple species (2010, neutral): “The mapping of the portion of HP corresponding to musculoskeletal phenotypes is described elsewhere [17].”
Uberon, an integrative multi-species anatomy ontology (2012, methodology): “We used the MPO [74] and the Human Phenotype Ontology [58] in a method analogous to the one described for GO, above.”
The Human Phenotype Ontology (2010, methodology): “About 50,000 annotations to HPO terms for 4779 diseases listed at the OMIM database are also available at the HPO website (11).”
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models (2012, methodology): “, 2011] including the Human Phenotype Ontology [Robinson et al., 2008] and the Mammalian Phenotype Ontology [Smith and Eppig, 2009].”
Clinical diagnostics in human genetics with semantic similarity searches in ontologies. (2009, methodology): “We developed the HPO in order to provide a standardized vocabulary of phenotypic abnormalities encountered in human disease.(10) For the comparisons described here, the version of the HPO from May 6, 2009 was used.”