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

Transfer learning across ontologies for phenome-genome association prediction.

Bioinformatics (Oxford, England) - 15 Feb 2017

Petegrosso Raphael, Park Sunho, Hwang Tae Hyun, Kuang Rui

Abstract excerpt

Motivation: To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computational Biology
Share to Gene Ontology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genome

Share to Models, Genetic

Share to Phenotype

Share to Protein Interaction Maps

Source

Identifiers

PMID: 27797759
DOI: 10.1093/bioinformatics/btw649

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

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

Transfer learning across ontologies for phenome-genome association prediction.PMID 27797759Read ↗

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.Semantic proximity Read ↗

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

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

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifierSemantic proximity Read ↗

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.Semantic proximity Read ↗

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier.Semantic proximity Read ↗

PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sourcesSemantic proximity Read ↗

Supervised-learning is an accurate method for network-based gene classificationSemantic proximity Read ↗

Integration of Human Protein Sequence and Protein-Protein Interaction Data by Graph Autoencoder to Identify Novel Protein-Abnormal Phenotype Associations.Semantic proximity Read ↗

Decipher the connections between proteins and phenotypes.Semantic proximity Read ↗

HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks.Semantic proximity Read ↗

End-to-end interpretable disease-gene association prediction.Semantic proximity Read ↗

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