基因调节网络推断基于因果发现,与图形神经网络集成
Ke Feng1, Hongyang Jiang1, Chaoyi Yin1
1School of Artificial Intelligence Jilin University Changchun China.
Quantitative biology (Beijing, China)
|February 12, 2026
概括
我们开发了GRINCD,这是一种使用图形表示和因果学习进行基因调节网络推断的新框架. GRINCD准确地识别了基因关系,并显示了癌症研究的潜力.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因调控网络 (GRN) 的推断对于理解生物系统至关重要.
- 以因果关系为灵感的方法为推断监管关系提供了比基于相关性的方法更合理的方法.
研究的目的:
- 提出GRINCD,一个新的GRN推理框架.
- 整合图表表示学习和因果不对称学习,以实现强大的GRN构建.
- 考虑线性和非线性监管关系.
主要方法:
- 利用图形神经网络来生成高质量的基因表征.
- 应用添加式噪声模型来预测基因对之间的因果调节.
- 设计和组装双通道架构,以提高预测准确度.
主要成果:
- 在各种数据集中,GRINCD与最先进的方法相比,表现优越或可比.
- 该框架有效地推断了基因调节关系,考虑到复杂的相互作用.
- 实验结果验证了拟议的GRINCD框架的稳定性和准确性.
结论:
- GRINCD为基因调节网络推断提供了一种新且有效的方法.
- 该框架在确定参与癌症发展的关键因素方面表现有前途.
- 这项工作有助于推进GRN构建和生物系统分析领域.
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