Cross-modal embedding integrator for disease-gene/protein association prediction using a multi-head attention mechanism.

ABSTRACT: Knowledge graphs, powerful tools that explicitly transfer knowledge to machines, have significantly advanced new knowledge inferences. Discovering unknown relationships between diseases and genes/proteins in biomedical knowledge graphs can lead to the identification of disease development mechanisms and new treatment targets. Generating high-quality representations of biomedical entities is essential for successfully predicting disease-gene/protein associations. We developed a computational model that predicts disease-gene/protein associations using the Precision Medicine Knowledge Graph, a biomedical knowledge graph. Embeddings of biomedical entities were generated using two different methods-a large language model (LLM) and the knowledge graph embedding (KGE) algorithm. The LLM utilizes

SUBMITTER: Chang M 

PROVIDER: S-EPMC11574662 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature