BioGraphFusion: Graph Knowledge Embedding for Biological Completion and Reasoning

TL;DR

BioGraphFusion is a novel framework that enhances biomedical knowledge graph completion and reasoning by integrating semantic and structural learning through a dynamic, reciprocal process, outperforming existing methods in key biomedical tasks.

Contribution

It introduces a synergistic approach combining tensor decomposition and LSTM-driven relation refinement for improved biomedical knowledge graph analysis.

Findings

Outperforms state-of-the-art KE, GNN, and ensemble models in biomedical tasks.

Demonstrates biologically meaningful pathway discovery in melanoma case study.

Provides open-source code and data for reproducibility.

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 structural learning. BioGraphFusion establishes a global semantic foundation via tensor decomposition, guiding an LSTM-driven mechanism to dynamically refine relation embeddings during graph propagation. This fosters adaptive interplay between semantic understanding and structural learning, further enhanced by query-guided subgraph construction and a hybrid scoring mechanism. Experiments across three key biomedical tasks demonstrate BioGraphFusion's superior performance over state-of-the-art KE, GNN, and ensemble models. A case study on Cutaneous Malignant Melanoma 1 (CMM1) highlights its ability to unveil biologically meaningful pathways. Availability and Implementation: Source code and all training data are freely available for download at https://github.com/Y-TARL/BioGraphFusion. Supplementary information: Supplementary data are available at Bioinformatics online.