用因果图神经网络找更稳定的疾病生物标志物
Causal Inference, Biomarker Discovery, Graph Neural Network, Feature Selection
- 融合因果推断与图神经网络，利用基因调控关系提升稳定性
- 在4个数据集上预测准确率均高，识别出比传统方法更稳定的标志物
- 适合精准医学中寻找可靠生物标志物的研究者使用
从高通量转录组数据中发现生物标志物对推动精准医学至关重要。然而，现有方法常忽视基因间调控关系，且跨数据集稳定性差，易将虚假相关误认为真实因果效应。为此，我们提出一种因果图神经网络（Causal-GNN）方法，将因果推断与多层图神经网络结合。核心创新在于通过因果效应估计识别稳定生物标志物，并采用基于GNN的倾向性评分机制，利用基因间调控网络。实验表明，该方法在四个不同数据集和四种独立分类器上均保持高预测精度，且能识别出比传统方法更稳定的生物标志物。本研究提供了一个鲁棒、高效且生物学可解释的生物标志物发现工具，具有广泛的医学应用潜力。
原文摘要 · Abstract (English)
Biomarker discovery from high-throughput transcriptomic data is crucial for advancing precision medicine. However, existing methods often neglect gene-gene regulatory relationships and lack stability across datasets, leading to conflation of spurious correlations with genuine causal effects. To address these issues, we develop a causal graph neural network (Causal-GNN) method that integrates causal inference with multi-layer graph neural networks (GNNs). The key innovation is the incorporation of causal effect estimation for identifying stable biomarkers, coupled with a GNN-based propensity scoring mechanism that leverages cross-gene regulatory networks. Experimental results demonstrate that our method achieves consistently high predictive accuracy across four distinct datasets and four independent classifiers. Moreover, it enables the identification of more stable biomarkers compared to traditional methods. Our work provides a robust, efficient, and biologically interpretable tool for biomarker discovery, demonstrating strong potential for broad application across medical disciplines.
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