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

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

BMC medical genomics - 23 Dec 2019

Gao Junning, Liu Lizhi, Yao Shuwei, Huang Xiaodi, Mamitsuka Hiroshi, Zhu Shanfeng

Abstract excerpt

BACKGROUND: As a standardized vocabulary of phenotypic abnormalities associated with human diseases, the Human Phenotype Ontology (HPO) has been widely used by researchers to annotate phenotypes of genes/proteins. For saving the cost and time spent on experiments, many computational approaches have been proposed. They are able to alleviate the problem to some extent, but their performances are still far from...

Read the complete abstract on PubMed

Topics

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

Share to Computational Biology
Share to Phenotype

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Protein Interaction Maps

Share to Semantics

Source

Open in PubMed Open full text

Identifiers

PMID: 31865916
DOI: 10.1186/s12920-019-0625-1
PMCID: PMC6927106

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

HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks.PMID 31865916Read ↗

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

HPODNets: deep graph convolutional networks for predicting human protein-phenotype associations.Semantic proximity Read ↗

HPOLabeler: improving prediction of human protein-phenotype associations by learning to rank.Semantic proximity Read ↗

Linking Phenotypes and Genotypes with Matrix Factorizations.Semantic proximity Read ↗

Co-clustering phenome-genome for phenotype classification and disease gene discovery.Semantic proximity Read ↗

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs.Semantic proximity Read ↗

DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature SelectionSemantic proximity Read ↗

Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human ProteinsSemantic proximity Read ↗

Weighted deep factorizing heterogeneous molecular network for genome-phenome association prediction.Semantic proximity Read ↗

Investigations on factors influencing HPO-based semantic similarity calculation.Semantic proximity Read ↗

Prediction of comorbid diseases using weighted geometric embedding of human interactome.Semantic proximity Read ↗

Integration of Biological Data via NMF for Identification of Human Disease-Associated Gene Modules through Multi-label ClassificationSemantic proximity Read ↗

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