ArticleBioinformatics (Oxford, England)2018
Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- INDIGENA: inductive prediction of disease-gene associations using phenotype ontologies.Bioinformatics (Oxford, England) · 2026Article
- Bridging bone and muscle: ontology-driven prioritization of candidate genes at the osteoporosis-sarcopenia interface by gene ontology reverse lookup.Frontiers in genetics · 2026Article
- A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops.Frontiers in plant science · 2024Article
- Predicting disease genes based on multi-head attention fusion.BMC bioinformatics · 2023Article
- Hetnet connectivity search provides rapid insights into how two biomedical entities are related.bioRxiv : the preprint server for biology · 2023Article
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- Development of a Knowledge Graph Embeddings Model for Pain.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Hetnet connectivity search provides rapid insights into how biomedical entities are related.GigaScience · 2022Article
- A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Article
- Integration of Protein-Protein Interaction Networks and Gene Expression Profiles Helps Detect Pancreatic Adenocarcinoma Candidate Genes.Frontiers in genetics · 2022Article
- Gene prediction of aging-related diseases based on DNN and Mashup.BMC bioinformatics · 2021Article
- HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball.Bioinformatics (Oxford, England) · 2021Article
- Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language.International journal of environmental research and public health · 2021Article
- KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species.Plant biotechnology journal · 2021Article
- A network-based machine-learning framework to identify both functional modules and disease genes.Human genetics · 2021Article
- Predicting candidate genes from phenotypes, functions and anatomical site of expression.Bioinformatics (Oxford, England) · 2021Article
- Application and evaluation of knowledge graph embeddings in biomedical data.PeerJ. Computer science · 2021Article
- Supervised learning is an accurate method for network-based gene classification.Bioinformatics (Oxford, England) · 2020Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
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2 authors.
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Abstract
Motivation: In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease's (or patient's) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results: We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprised of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network. Availability and implementation: https://github.com/bio-ontology-research-group/SmuDGE.
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