X-intNMF:一个跨和内部Omics规范的NMF框架,用于多Omics集成
Tien-Thanh Bui1,2, Rui Xie3,4, Wei Zhang1,2
1Department of Computer Science, University of Central Florida, Orlando, FL 32816, United States.
Bioinformatics (Oxford, England)
|January 28, 2026
概括
我们开发了X-intNMF,这是一种用于多omics数据集成的新型计算方法. 这种方法有效地模拟了跨OMIC和内部OMIC的相互作用,改善了疾病预测和预后价值.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 多学科数据集成对于理解复杂疾病至关重要.
- 现有的方法往往忽视了跨omics的相互作用,限制了生物学洞察力.
- 需要模型来捕捉内部和跨经济关系.
研究的目的:
- 提出X-intNMF,一个网络规范的非负矩阵分解 (NMF) 模型,用于多omics集成.
- 为了明确地纳入内部和跨领域的功能交互网络.
- 增强生物相互作用的代表性,提高整合质量.
主要方法:
- 开发了X-intNMF,这是一个网络规范化的NMF模型.
- 集成的mRNA表达,microRNA表达和DNA甲基化数据.
- 应用该模型来预测癌症表型和临床结果.
主要成果:
- 在预测乳腺癌表型和分类肺癌/卵巢癌结果方面,X-intNMF的表现优于最先进的方法.
- 废弃性研究证实了交叉和内部OMIC互动的重要性.
- 生存分析显示了25个TCGA癌症数据集的整体生存率和无病状态的强有力的预后价值.
结论:
- X-intNMF有效地模拟了多层分子相互作用,以改善多omics集成.
- 该模型展示了可解释性,稳定性和可扩展性.
- X-intNMF为推进精准医学和理解复杂疾病提供了巨大的潜力.
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