仿分子动理学数据聚类法在基因表达数据上的应用

为了识别出可能具有诊断力的特征基因,常常使用聚类的方法对基因表达数据进行分析,而仿分子动理学聚类法通过仿效分子间的作用力机制能达到对数据聚类的目的。仿分子动理学聚类技术不需要预设簇个数,且可用于估计数据中的簇个数。该方法被应用于基因表达数据,结合相关指标用以估计数据中存在的簇个数和发现可能具有诊断力的特征基因。实验与分析结果显示了仿分子动理学聚类技术具有良好的知识挖掘能力。

Applications of molecular-kinetic-theory-based clustering approach on gene expression data

In order to find possible diagnostic genes that may typically assist in disease diagnosis, clustering technologies are always used to analyze gene expression data. Molecular-kinetic-theory-based clustering approach is a new and effective clustering technique. It finds data clusters by following the molecular kinetic mechanism. This dynamic clustering approach does not require presetting the number of clusters and can be used to estimate the number of clusters. The authors applied the method on gene expression data to estimate the number of clusters and possible diagnostic genes according to relevant clustering criteria. The simulation results and analysis verify the good knowledge discovery ability of this approach.