BioGraphFusion: graph knowledge embedding for biological completion and reasoning.

ABSTRACT:

Motivation

Biomedical knowledge graphs (KGs) are crucial for drug discovery and disease understanding, yet their completion and reasoning are challenging. Knowledge embedding (KE) methods capture global semantics but struggle with dynamic structural integration, while graph neural networks (GNNs) excel locally but often lack semantic understanding. Even ensemble approaches, including those leveraging language models, often fail to achieve a deep, adaptive, and synergistic co-evolution between semantic comprehension and structural learning. Addressing this critical gap in fostering continuous, reciprocal refinement between these two aspects in complex biomedical KGs is paramount.

Results

We introduce BioGraphFusion, a novel framework for deeply synergistic semantic and struct

SUBMITTER: Lin Y 

PROVIDER: S-EPMC12311287 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature