Multi-association driven gene risk prediction based on graph convolutional network

doi:10.1016/j.asoc.2026.116092

2026-07-29

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DOI：10.1016/j.asoc.2026.116092

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摘要

En 中文

• 提出整合局部和全局患者关系的多关联图卷积网络（GCN）框架用于风险预测。 • 开发了加权图构建算法，用于量化跨临床维度的特征重要性。 • 验证样本间的结构关系可增强分类性能（AUC 0.8914，准确率86.78%）。 • 展示了3层模型在保持计算效率的同时优化性能。 • 建立了经济有效的MammaPrint替代方案，并提供可解释的图分析方法。

期刊

Applied Soft Computing

IF:

6.6

论文数:

1.4W

被引数:

4.8W

机构

ocean university of china

学者数:

3.1W

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被引数: 21

Peking Union Medical College Hospital and Chinese Academy of Medical Sciences

学者数:

论文数: 9

被引数: 2

fuzhou university

学者数:

3.3W

论文数: 2.1W

被引数: 31

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