一个使用多标签指导学习和多规模融合的多omics整合框架
Yuze Li1, Yinghe Wang1, Tao Liang1
1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street 2699, 130012 Jilin, China.
Briefings in bioinformatics
|September 25, 2025
概括
我们开发了mmMOI,这是一种用于多omics集成的新框架,可以避免手动选择功能. 这种方法通过提高分类性能和识别关键生物标志物来增强对复杂疾病的理解.
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:
- 高通量测序产生了大量的OMIC数据,需要多OMIC集成用于复杂疾病研究.
- 当前的多omics集成方法面临的局限性包括手动特征选择,狭窄的适用性,以及对样本间和跨omics相互作用的不充分捕获.
研究的目的:
- 引入mmMOI,这是一个旨在克服现有局限性的多omics集成的端到端框架.
- 通过直接处理原始omics数据而不是手动选择功能来增强模型的解释性和减少偏差.
主要方法:
- 开发了一种多标签引导的多视图图形神经网络,用于跨数据集的自适应式omics数据表示学习.
- 设计了一个多层次的注意力融合网络,整合全球和本地注意力,以准确地整合多领域的数据.
- 采用一个端到端的框架处理原始高维的OMICS数据.
主要成果:
- 在分类任务中,mmMOI显著超过了最先进的方法.
- 证明mmMOI在各种生物环境和测序技术中具有很高的稳定性和适应性.
- 成功识别了与疾病相关的关键生物标志物,提高了生物解释性.
结论:
- mmMOI提供了一个强大的和可解释的解决方案,用于多omics集成.
- 该框架改善了复杂疾病的分类性能和生物标志物发现.
- mmMOI为推进生物研究和临床应用提供了宝贵的工具.
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