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使用多视图图形卷积神经网络对lncRNAs和疾病的关联预测

Wei Zhang¹, Yifu Zeng¹, Xiaowen Xiang¹

¹College of Computer Science and Engineering, Changsha University, Changsha, Hunan, China.

Frontiers in genetics

|April 30, 2025

概括

预测长非编码RNA (lncRNA) 疾病关联是很困难的. MVIGCN是一个新型的图形卷积网络,集成多模式数据,以准确识别疾病的lncRNA生物标志物.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

长非编码RNAs (lncRNAs) 是生理过程的关键调节者,并形成复杂的疾病相关网络.
由于复杂的网络结构和数据稀疏性,预测 lncRNA-疾病关联是具有挑战性的.

研究的目的:

开发一种先进的计算方法,用于预测长非编码RNA (lncRNA) 与疾病的关联.
解决现有方法在处理网络复杂性和孤立生物实体方面的局限性.

主要方法:

提出了MVIGCN,这是一个集成多式联网数据的图形卷积网络 (GCN) 框架.
构建了一个包含疾病语义,lncRNA相似性和miRNA-lncRNA-疾病相互作用的异质网络.
使用注意力机制的深度学习来建模拓特征和多尺度关系.

主要成果:

通过对异质网络中的关键节点和边缘进行优先排序,MVIGCN证明了更高的预测准确性.
交叉验证证实,与单视图预测方法相比,可靠性有所提高.
成功识别了可能与疾病相关的 lncRNA 生物标志物.

结论:

MVIGCN提供了一个强大的,可扩展的计算策略来解码 lncRNA 功能在疾病生物学.
该方法推进了基于网络的方法,用于识别治疗点和理解疾病机制.
突出了多式联通数据集成的潜力,以改善 lncRNA-疾病关联预测.

关键词:

深度学习是一种深度学习.图表卷积网络卷积网络在 lncRNA-miRNA 中.多视图数据多视图数据类似性网络的类似性网络.

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