半监督深度矩阵分解模型用于聚类多omics数据
Khanh Luong1, Nirav Joshi1, Richi Nayak2
1QUT Centre for Data Science, School of Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia.
Computer methods and programs in biomedicine
|October 14, 2025
概括
本研究介绍了一种半监督深度非负矩阵因子化模型 (SSD-MO) 用于多omics数据集成. 通过有效利用标记和未标记样本,SSD-MO显著提高了集群精度和性能.
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.
- 数据科学数据科学数据科学
背景情况:
- 多主题数据由于高维度,稀疏性和噪音而带来挑战.
- 传统的方法在噪音,可解释性和捕捉非线性模式方面扎.
- 现有的多视图非负矩阵分解方法在很大程度上没有监督.
研究的目的:
- 开发一个强大的模型,用于多omics数据集成和集群.
- 解决处理复杂,高维度生物数据的现有方法的局限性.
- 为了提高性能,利用标记和未标记的样本.
主要方法:
- 拟议的SSD-MO (半监督深度非负矩阵分解) 模型.
- 结合了半监督学习与一个深层次的因素化框架.
- 结合了保留几何结构,直角性和多样性的约束.
主要成果:
- SSD-MO显著提高了6个多omics数据集的集群精度.
- 与未经监督的基线相比,F-score提高了9%至24%.
- 在精确度 (64%-73%) 和回忆 (70%-88%) 方面表现出强的性能.
结论:
- SSD-MO为多omics数据集成提供了一个强大的框架.
- 该方法对基因组学和精密医学的应用有希望.
- 通过有效利用标记和未标记的数据来提高聚类性能.
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