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基于DINNSM的聚类算法及其在基因表达数据分析中的应用

Zongjin Li¹, Changxin Song², Jiyu Yang³

¹Department of Computer, Qinghai Normal University, Xining, China.

Technology and health care : official journal of the European Society for Engineering and Medicine

|May 17, 2024

概括

一种新方法,双指数近邻相似度测量 (DINNSM),改善了基因共表达模块的识别. DINNSM提供比传统相似度更准确的生物学见解.

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
系统生物学系统生物学

背景情况:

有效的相似度测量对于生物学上有意义的聚类至关重要.
现有的方法难以应对生物系统中的复杂性和相互作用.

研究的目的:

开发一种用于增强基因模块发现的新型相似度测量方法.
为了提高基因表达数据聚类的生物相关性.

主要方法:

提出了双指数近邻相似度测量 (DINNSM) 算法.
使用皮尔森或斯皮尔曼相关性计算基因相似性.
构建了一个最近邻表,并重建了相似性矩阵.

主要成果:

在5个基因表达数据集中,DINNSM的表现优于5个常见的相似度.
使用DINNSM的聚类结果显示出卓越的性能.
在识别基因共同表达模块方面证明了更好的准确性.

结论:

DINNSM为基因相互作用提供了更准确的生物学见解.
有助于识别更多生物相关的基因共表达模块.

关键词:

集群集成是指集群集成.同表达模块的共同表达模块基因表达数据基因表达数据相似性衡量措施相似性衡量措施

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