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Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers
Chen, Xuexin Cai, Ruichu Huang, Zhengting Li, Zijian Zheng, Jie Wu, Min
Chen, Xuexin
Cai, Ruichu
Huang, Zhengting
Li, Zijian
Zheng, Jie
Wu, Min
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Synthetic lethality (SL) is a promising gene interaction for cancer therapy. Recent SL prediction methods integrate knowledge graphs (KGs) into graph neural networks (GNNs) and employ attention mechanisms to extract local subgraphs as explanations for target gene pairs. However, attention mechanisms often lack fidelity, typically generate a single explanation per gene pair, and fail to ensure trustworthy high-order structures in their explanations. To overcome these limitations, we propose Diverse Graph Information Bottleneck for Synthetic Lethality (DGIB4SL), a KG-based GNN that generates multiple faithful explanations for the same gene pair and effectively encodes high-order structures. Specifically, we introduce a novel DGIB objective, integrating a determinant point process constraint into the standard information bottleneck objective, and employ 13 motif-based adjacency matrices to capture high-order structures in gene representations. Experimental results show that DGIB4SL outperforms state-of-the-art baselines and provides multiple explanations for SL prediction, revealing diverse biological mechanisms underlying SL inference.
Citation
X. Chen, R. Cai, Z. Huang, Z. Li, J. Zheng, and M. Wu, “Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers,” Brief Bioinform, vol. 26, no. 2, Mar. 2025, doi: 10.1093/BIB/BBAF142.
Source
Brienfings in Bioinformatics
Conference
Keywords
Synthetic lethality, Machine learning explainability, Graph neural network, Information bottleneck
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Source
Publisher
Oxford University Press