Explainable/interpretable artificial intelligence and knowledge graph neural network for synthetic lethality prediction: overview, challenges, and future directions.

Integrating knowledge graph neural networks with multi-omics data enhances synthetic lethality prediction accuracy in cancer research.

• Why it matters: Mapping synthetic lethal gene pairs experimentally is limited by the enormous combinatorial complexity and high costs, creating a need for reliable computational methods to accelerate discovery.
• What they did: The review evaluates how various GNN architectures incorporate biological data, compares interpretability approaches, and explores the potential of Generative AI and large language models for SL prediction.
• The result: This overview highlights that combining GNNs with heterogeneous knowledge graphs improves biological context capture, and emphasizes the importance of model interpretability and AI advances for clinical trust and future research directions.