自动GRN:用于基因调节网络推理的自动图形神经网络框架
IEEE journal of biomedical and health informatics
|September 16, 2025
概括
自动GRN自动设计最佳的图形神经网络 (GNN) 架构,用于从单细胞RNA测序数据中推断基因调节网络 (GRN),从而提高各种数据集的准确性.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 基因调控网络 (GRN) 的推断对于理解生物过程和疾病至关重要.
- 单细胞RNA测序 (scRNA-seq) 数据被广泛用于GRN推断,但存在稀疏性,噪声和异质性等挑战.
- 由于固定的架构,现有的图形神经网络 (GNN) 方法难以在各种scRNA-seq数据集中进行概括.
研究的目的:
- 开发一个自动化框架,AutoGRN,用于优化GNN架构用于GRN推理.
- 解决固定GNN架构在处理scRNA-seq数据的复杂性方面的局限性.
- 提高GRN推理方法的准确性和通用性.
主要方法:
- AutoGRN使用由信息局限的遗传搜索算法来探索和识别最佳的GNN架构.
- 该框架将影响GRN推理性能的各种组件纳入其搜索空间.
- 自动化方法使GNN架构适应单个scRNA-seq数据集的特定特征.
主要成果:
- 与现有的GRN推断方法相比,AutoGRN显示出更高的预测准确性.
- 拟议的框架在多个公开的scRNA-seq数据集中显示出强大的性能.
- 自动化架构搜索有效地处理数据稀疏性,噪音和异质性.
结论:
- 自动GRN为基因调节网络推断提供了有效和可适应的解决方案.
- 自动化的GNN架构优化显著提高了GRN推理准确性和通用性.
- 这一框架将从复杂的单细胞表达数据中推进基因相互作用的分析.
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