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全球-本地意识异质图对比学习用于miRNA-基因-疾病网络中的多面关联预测

Yuxuan Si^1,2, Zihan Huang², Zhengqing Fang^1,2

¹Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road, 310016 Zhejiang, China.

Briefings in bioinformatics

|September 10, 2024

概括

这项研究引入了全球本地意识的异构图对比学习 (GlaHGCL),以预测微RNA (miRNA),基因和疾病之间的关联. GlaHGCL通过利用全球和本地图形结构来提高预测准确性,克服数据稀疏性的挑战.

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
系统生物学系统生物学

背景情况:

了解微RNA (miRNA),基因和疾病之间的关联对于破译分子机制,诊断和治疗开发至关重要.
在生物图表中计算链接预测为实验方法提供了具有成本效益的替代方案.
现有的方法往往侧重于特定的关联 (例如,miRNA疾病),缺乏全面的方法来进行多方面的预测.

研究的目的:

提出一种新的框架,全球本地意识的异质图对比学习 (GlaHGCL),用于预测生物网络中的复杂关联.
为了应对稀疏,杂的异质图形和有限的高质量数据在生物关联预测中的挑战.
通过整合全球和本地对比学习策略来增强异构图中的节点嵌入.

主要方法:

开发了GlaHGCL,这是一个结合全球和本地对比学习的框架,用于异质图形表示学习.
全球对比学习对齐原始和增强图形的表示,以提高对噪声的稳定性.
当地对比学习强制执行功能上相似的节点之间的表示一致性,利用数据异质性和减轻稀缺性.

主要成果:

在预测miRNA-疾病关联 (MDA),miRNA-标相互作用 (MTIs) 和疾病基因关联 (DGA) 方面,GlaHGCL显著超过了最先进的方法.
案例研究验证了GlaHGCL在精确识别miRNA,基因和疾病之间新兴关联的能力.
该框架有效地改进了节点嵌入,提高了下游任务的预测准确性.

结论:

GlaHGCL为多面生物关联预测提供了一种强大而有效的方法.
提出的方法成功地解决了异质生物网络中的数据稀疏性和噪声问题.
这一框架提升了通过计算分析发现新生物学见解和治疗目标的潜力.

关键词:

疾病基因关联图表对比的学习学习.不同质的数据来源.miRNA疾病关联.微RNA基因相互作用

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