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PSO-CFDP: A Particle Swarm Optimization-Based Automatic Density Peaks Clustering Method for Cancer Subtyping.
Human Heredity ( IF 1.8 ) Pub Date : 2019-08-14 , DOI: 10.1159/000501481
Xuhui Zhu 1 , Junliang Shang 2, 3 , Yan Sun 1 , Feng Li 1 , Jin-Xing Liu 1 , Shasha Yuan 1
Affiliation  

Cancer subtyping is of great importance for the prediction, diagnosis, and precise treatment of cancer patients. Many clustering methods have been proposed for cancer subtyping. In 2014, a clustering algorithm named Clustering by Fast Search and Find of Density Peaks (CFDP) was proposed and published in Science, which has been applied to cancer subtyping and achieved attractive results. However, CFDP requires to set two key parameters (cluster centers and cutoff distance) manually, while their optimal values are difficult to be determined. To overcome this limitation, an automatic clustering method named PSO-CFDP is proposed in this paper, in which cluster centers and cutoff distance are automatically determined by running an improved particle swarm optimization (PSO) algorithm multiple times. Experiments using PSO-CFDP, as well as LR-CFDP, STClu, CH-CCFDAC, and CFDP, were performed on four benchmark data-sets and two real cancer gene expression datasets. The results show that PSO-CFDP can determine cluster centers and cutoff distance automatically within controllable time/cost and, therefore, improve the accuracy of cancer subtyping.

中文翻译:

PSO-CFDP:用于癌症亚型的基于粒子群优化的自动密度峰聚类方法。

癌症亚型对于癌症患者的预测,诊断和精确治疗非常重要。已经提出了许多用于癌症分型的聚类方法。2014年,提出了一种名为“通过快速搜索和发现密度峰值进行聚类”(CFDP)的聚类算法,并在《科学》杂志上发表了该算法,该算法已应用于癌症亚型研究并获得了诱人的结果。但是,CFDP需要手动设置两个关键参数(集群中心和截止距离),而很难确定它们的最佳值。为了克服这一局限性,本文提出了一种自动聚类方法PSO-CFDP,该方法通过多次运行改进的粒子群算法(PSO)自动确定聚类中心和截止距离。使用PSO-CFDP以及LR-CFDP,STClu,CH-CCFDAC和CFDP在四个基准数据集和两个真实的癌症基因表达数据集上进行。结果表明,PSO-CFDP可以在可控制的时间/成本内自动确定聚类中心和截止距离,从而提高了癌症分型的准确性。
更新日期:2019-11-01
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