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根据ceRNA网络的更高阶结构来预测疾病关联

Zhaoliang Chai¹, Ying Su¹, Xuecong Tian¹

¹College of Computer Science and Technology, Xinjiang University, Urumqi 830046, Xinjiang, China.

Briefings in bioinformatics

|October 4, 2025

概括

我们开发了CERDA-HOSR,这是一种新的计算方法,使用更高阶图的注意力网络来预测竞争性内源性RNA (ceRNA) 网络中的疾病关联. 这种方法通过优化复杂的生物数据的负采样来提高准确性.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

竞争性内源RNA (ceRNA) 网络是参与各种生理和病理过程的关键转录后调节器.
目前关于ceRNA网络的研究主要集中在单个RNA类型和疾病关联上,疾病预测的探索有限.
有必要研究ceRNA网络的潜力,以准确预测疾病.

研究的目的:

提出CERDA-HOSR,一种用于识别使用更高顺序图表注意力网络的ceRNA网络疾病关联的计算方法.
为了应对生物网络中高阶复杂性和样本不平衡的挑战.
提高ceRNA网络疾病关联挖掘模型的概括能力和预测准确度.

主要方法:

利用更高阶图形卷积网络来聚合邻近信息用于RNA和疾病表示学习.
设计了一个更高层次的负采样策略,通过整合网络结构和更高层次的邻里关系来优化负样品质量.
使用LightGBM计算基于学习嵌入的ceRNA网络疾病关联概率.

主要成果:

通过广泛的模拟实验证明了CERDA-HOSR的优势.
在涉及心血管疾病,急性髓性白血病和乳头甲状腺癌的案例研究中验证了CERDA-HOSR的实际应用.
废弃实验和探索性分析证实了拟议方法的稳定性.

结论:

通过利用ceRNA网络与疾病的关联,CERDA-HOSR为疾病预测和生物标志物查提供了有效的计算工具.
该方法通过解决网络复杂性和样本不平衡,提高了准确性和概括性.
强调ceRNA网络在推进疾病预测策略方面的巨大潜力.

关键词:

ceRNA 网络的 ceRNA 网络疾病预测疾病预测图表注意力网络网络图表卷积神经网络卷积神经网络

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