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

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities.

BMC bioinformatics - 7 Oct 2020

Fernando Pasan C, Mabee Paula M, Zeng Erliang

Abstract excerpt

BACKGROUND: Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for...

Read the complete abstract on PubMed

Topics

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

Share to Area Under Curve
Share to Databases, Protein

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Gene Regulatory Networks

Share to Protein Interaction Mapping

Share to Protein Interaction Maps

Share to ROC Curve

Share to User-Computer Interface

Share to Zebrafish

Source

Open in PubMed Open full text

Identifiers

PMID: 33028186
DOI: 10.1186/s12859-020-03773-2
PMCID: PMC7542696

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities.PMID 33028186Read ↗

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entitiesSemantic proximity Read ↗

Predicting candidate genes from phenotypes, functions, and anatomical site of expressionSemantic proximity Read ↗

Predicting candidate genes from phenotypes, functions and anatomical site of expression.Semantic proximity Read ↗

Constructing an integrated gene similarity network for the identification of disease genesSemantic proximity Read ↗

IMA: Identifying disease-related genes using MeSH terms and association rules.Semantic proximity Read ↗

Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network.Semantic proximity Read ↗

Constructing a gene semantic similarity network for the inference of disease genes.Semantic proximity Read ↗

Interoperability between phenotype and anatomy ontologies.Semantic proximity Read ↗

Inference of gene-phenotype associations via protein-protein interaction and orthology.Semantic proximity Read ↗

Linking human diseases to animal models using ontology-based phenotype annotation.Semantic proximity Read ↗

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model.Semantic proximity Read ↗

PhenomeNET: a whole-phenome approach to disease gene discovery.Semantic proximity Read ↗

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