Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

The biological coherence of human phenome databases.

American journal of human genetics - 1 Dec 2009

Oti Martin, Huynen Martijn A, Brunner Han G

Abstract excerpt

Disease networks are increasingly explored as a complement to networks centered around interactions between genes and proteins. The quality of disease networks is heavily dependent on the amount and quality of phenotype information in phenotype databases of human genetic diseases. We explored whi...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Chromosome Mapping
Share to Databases, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to Genome, Human

Share to Models, Genetic

Share to Models, Statistical

Share to Multigene Family

Source

Open in PubMed Open full text

Identifiers

PMID: 20004759
DOI: 10.1016/j.ajhg.2009.10.026
PMCID: PMC2790572

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026. Prepared record publication-20004759-2026-09-02.2. Manifest publication-graph-cohort-2026-09-02.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

The biological coherence of human phenome databases.PMID 20004759Read ↗

HPOSim: an R package for phenotypic similarity measure and enrichment analysis based on the human phenotype ontology.Semantic proximity Read ↗

Towards prediction and prioritization of disease genes by the modularity of human phenome-genome assembled network.Semantic proximity Read ↗

A text-mining analysis of the human phenome.Semantic proximity Read ↗

PhenoMiner: from text to a database of phenotypes associated with OMIM diseases.Semantic proximity Read ↗

The Human Phenotype Ontology in 2017.Semantic proximity Read ↗

PhenoSS: Phenotype semantic similarity-based approach for rare disease prediction and patient clusteringSemantic proximity Read ↗

Disease classification: from phenotypic similarity to integrative genomics and beyond.Semantic proximity Read ↗

OMIM.org: leveraging knowledge across phenotype–gene relationshipsSemantic proximity Read ↗

[Diagnostics in human genetics : Integration of phenotypic and genomic data].Semantic proximity Read ↗

The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease.Semantic proximity Read ↗

The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease.Semantic proximity Read ↗

PhenUMA: a tool for integrating the biomedical relationships among genes and diseases.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.