联合相似性非负矩阵因子化模型用于识别瘤中复发相关的关联模式
Jin Deng1, Junjie Lan1, Ruolan Du1
1College of Mathematics and Informatics, South China Agricultural University, No. 483 Wushan Street, Tianhe District, Guangzhou 510642, China.
Briefings in bioinformatics
|November 3, 2025
概括
这项研究引入了一种新的多式联络数据分析模型,用于识别瘤复发生物标志物. 该方法有效地整合了病理图像和基因表达数据,改善了对瘤复发模式的理解.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 高瘤复发率阻碍了精准医学的进步.
- 现有的多式联络数据分析往往忽视了基因通路的参与,限制了解释性.
- 确定瘤复发的可靠生物标志物对于有效的治疗策略至关重要.
研究的目的:
- 开发一种新的多式联络数据分析框架,用于识别瘤复发生物标志物.
- 通过使用集成数据来提高关联模型的可解释性和效率.
- 为揭示瘤复发复杂模式的新见解.
主要方法:
- 提出了一种联合相似性非负矩阵因子化 (JSNMF) 模型,整合了病理图像和基因表达数据.
- 利用相似性网络融合模型从三个数据模式计算融合矩阵.
- 纳入主要组件分析 (PCA) 预先信息和应用网络规范化约束到JSNMF模型.
- 通过稀疏的直角性约束提高了模型效率.
主要成果:
- 该JSNMF模型成功地确定了与复发相关的常见模块,包括细胞特征,基因和通路.
- 生物信息学分析揭示了与免疫细胞透水平相关的潜在生物标志物,用于复发诊断.
- 证明,结合先前的知识可以提高搜索跨多式联运数据的联合模式的效率.
结论:
- 拟议的JSNMF方法为多式联络数据中挖掘特定任务的关联提供了新的视角.
- 这项研究增强了与瘤复发相关的遗传分子特征之间的关联模式的理解.
- 这些发现有助于开发更有效的精准医学策略,通过识别强大的复发生物标志物.
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