基于对比学习的基因监管网络推理的多omics数据集成框架
IEEE transactions on computational biology and bioinformatics
|August 14, 2025
概括
我们开发了CLMOGRI,这是一个新的框架,用于从多omics数据中推断基因调节网络 (GRNs). 该工具有效地整合了各种生物数据,以确定关键的监管要素并提高网络的可解释性.
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 (GRNs) 对于在分化过程中保持细胞稳定性和功能至关重要.
- 现有的方法很难有效地整合多omics数据用于细胞类型特定的GRN推断.
研究的目的:
- 引入CLMOGRI,这是一个新的多omics框架,用于推断转录因子-基因调控网络.
- 增强多种omics数据的集成,以实现更准确的GRN构建.
主要方法:
- CLMOGRI利用异质网络和对比学习将多omics数据嵌入到统一的功能空间中.
- 随机步行技术用于特征提取和节点相似性测量.
- 对比学习用于预测基因调节关系.
主要成果:
- 在精度回忆曲线下的区域 (AUPR) 和F-Score.中,CLMOGRI显著优于现有的基线方法.
- 该框架有效地捕获了多omics信息,以进行强大的GRN推断.
- 在GRN中,CLMOGRI成功地识别了关键节点和模块,提高了可解释性.
结论:
- CLMOGRI提供了一个强大的和可解释的方法,用于多omics GRN推断.
- 该框架提高了我们对复杂的生物相互作用和细胞调节的理解.
- 在生物系统分析中,CLMOGRI促进了GRNs的实用性.
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