Multimodal GAN Integrating Hypergraph and Knowledge Graph Representations for Synthetic Lethality

Wei Zhang, Yunlong Wang, Zhijuan Li, Yong Liu, Xiaokun Li, Jiachen Ma

Abstract: Due to the toxicity of traditional treatments and the emergence of resistance in targeted therapies, cancer therapies confront significant challenges. Synthetic lethality (SL) presents a promising avenue for precision medicine by selectively inhibiting genes that partner with tumor-specific mutations. Traditional SL prediction methods are constrained by binary interaction assumptions, making it challenging to capture complex gene-gene interactions and address the imbalance inherent in multimodal data. This study introduces the multimodal generative adversarial network integrating hypergraph and knowledge graph representations for SL prediction to overcome this limitation. This model innovatively incorporates the hypergraph to represent SL relationships and leverages graph neural networks to character higher-order gene interactions through hyperedge, thereby overcoming the limitations of traditional binary interaction assumptions. Additionally, a dynamic weight allocation mechanism is designed, utilizing attention networks to quantify the heterogeneous contributions of multimodal data, addressing the challenges posed by data imbalance. Finally, an adversarial training approach is employed, where the generator dynamically produces negative samples to mitigate issues related to sample scarcity and annotation noise. Experiments conducted on the SynLethDB 2.0 dataset demonstrate that MGHK4SL significantly outperforms existing methods based on traditional machine learning and graph neural networks in multimodal integration and higher-order relationship mining. This advancement offers interpretable biomarker networks for precision cancer therapy. Our code has been released at https://github.com/wyl20181914/MGHK4SL.

External IDs:dblp:conf/icic/ZhangWLLLM25