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

Predicting candidate genes from phenotypes, functions and anatomical site of expression.

Bioinformatics (Oxford, England) - 5 May 2021

Chen Jun, Althagafi Azza, Hoehndorf Robert

Abstract excerpt

MOTIVATION: Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease-gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated...

Read the complete abstract on PubMed

Topics

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

Share to Biological Ontologies
Share to Machine Learning
Share to Mice

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Share to Software

Source

Open in PubMed Open full text

Identifiers

PMID: 33051643
DOI: 10.1093/bioinformatics/btaa879
PMCID: PMC8248315

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Predicting candidate genes from phenotypes, functions and anatomical site of expression.PMID 33051643Read ↗

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

Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypesSemantic proximity Read ↗

INDIGENA: inductive prediction of disease-gene associations using phenotype ontologies.Semantic proximity Read ↗

An inductive, supervised approach for predicting gene–disease associations using phenotype ontologiesSemantic proximity Read ↗

Phen2Disease: A Phenotype-driven Semantic Similarity-based Integrated Model for Disease and Gene PrioritizationSemantic proximity Read ↗

Improving disease gene prioritization by comparing the semantic similarity of phenotypes in mice with those of human diseases.Semantic proximity Read ↗

Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks.Semantic proximity Read ↗

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learningSemantic proximity Read ↗

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.Semantic proximity Read ↗

A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization.Semantic proximity Read ↗

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning.Semantic proximity Read ↗

How much do model organism phenotypes contribute to the computational identification of human disease genes?Semantic proximity Read ↗

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