多视图增强张量核规范和局部约束模型用于癌症集群和特征基因选择
Qian Qiao1, Sha-Sha Yuan1, Junliang Shang1
1School of Computer Science, Qufu Normal University, Rizhao, China.
概括
这项研究引入了一种新的多视图增强张量核规范和局部约束 (MVET-LC) 模型用于癌症研究. 该模型有效地集群癌症样本,并使用多omics数据分析识别特征基因.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 多omics数据分析对于推进癌症研究至关重要.
- 了解癌症和基因之间的相关性需要复杂的分析方法.
- 现有的方法可能无法充分利用多视图数据的一致性和互补性.
研究的目的:
- 开发一种用于集群癌症样本和识别特征基因的模型.
- 通过分析多视图癌症omics数据来揭示癌症和基因之间的相关性.
- 加强生物研究中多视图数据的利用.
主要方法:
- 提出了多视图增强张量核规范和局部约束 (MVET-LC) 模型.
- 为了提高灵活性,引入了张量核规范的增强部分总和.
- 整合了总变化规范化和乘数的交替方向方法以进行优化.
主要成果:
- MVET-LC模型有效地利用了多视图数据的一致性和互补性.
- 与现有方法相比,该模型在聚类癌症样本和识别特征基因方面表现出卓越的性能.
- 实验验证证证实了拟议的MVET-LC方法的有效性.
结论:
- MVET-LC模型通过整合多视图omics数据,为癌症研究提供了一个强大的新工具.
- 这种方法增强了与癌症相关的基因的识别和样本聚类.
- 这些发现支持精密瘤学中基于张量器的先进方法的潜力.
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