Sep 26, 2017 · Thus, we propose an approach in which disease-gene co-occurrences and gene-gene interactions are represented in an RDF graph. A machine learning ...
Abstract. In the context of personalized medicine, text mining meth- ods pose an interesting option for identifying disease-gene associations,.
This work proposes an approach in which disease- gene co-occurrences and gene-gene interactions are represented in an RDF graph and a machine learning-based ...
Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies · Biomedical knowledge graph embeddings for personalized medicine: ...
May 14, 2024 · KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both ...
Missing: Document | Show results with:Document
Jun 14, 2024 · Conclusion. In this article, a deep learning framework called ModulePred is presented for predicting disease-gene associations. ModulePred ...
Sep 26, 2017 · Predicting Disease-Gene Associations using. Cross-Document Graph-based Features. Hendrik ter Horst1, Matthias Hartung1, Roman Klinger1,2 ...
May 14, 2024 · Abstract. Knowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low ...
Jun 14, 2021 · We developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes.
We developed a multi-graph representation learning-based ensemble model, named MGREL to predict gene-disease associations.
Missing: Document | Show results with:Document