Semantic methods for prioritising causative variants in complex (polygenic / oligogenic) disease using phenotype ontologies and gene network embeddings.
- Start
- 2019
- End
- 2021
- Tags
- machine learning, ontologies, complex disease, rare disease, variant prioritization, semantic similarity, phenotype reasoning
Connections
funded by borg:fundedBy
team borg:hasMember
publications borg:producedPub
- Ontology-based prediction of cancer driver genes
- DDIEM: drug database for inborn errors of metabolism
- DeepGOPlus: improved protein function prediction from sequence
- Klarigi: Characteristic explanations for semantic biomedical data
- Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies
- PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research
- Using SPARQL to Unify Queries over Data, Ontologies, and Machine Learning Models in the PhenomeBrowser Knowledgebase
- Improved characterisation of clinical text through ontology-based vocabulary expansion
Referenced by
on project borg:onProject
from project borg:fromProject
- OPA2Vec
- DeepPheno
- PhenomeNET-VP
- DeepSVP
- EmbedPVP
- STARVar
- INDIGENA
- predCAN
- DeepViral
- SmuDGE
- PhenomeNet
- Ontology-based prediction of cancer driver genes
- DDIEM: drug database for inborn errors of metabolism
- DeepGOPlus: improved protein function prediction from sequence
- Klarigi: Characteristic explanations for semantic biomedical data
- Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies
- PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research
- Using SPARQL to Unify Queries over Data, Ontologies, and Machine Learning Models in the PhenomeBrowser Knowledgebase
- Improved characterisation of clinical text through ontology-based vocabulary expansion
related projects borg:linkedProject
Open in the interactive graph →
JSON-LD (this resource)
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Machine-readable copy: data.jsonld. Full dataset: kg.jsonld.