联合聚类非线性相关的时序基因表达数据

为聚类非线性相关的数据对象,引入广义信息论中二次互信息作为相似性度量,利用矩阵理论降低了二次互信息的计算量,并结合滑动窗口技术,建立了一种时序数据非线性相关模型.在此基础上提出了适用于时序基因表达数据的确定性联合聚类算法MI-TSB.该算法将时序数据转化为抽象字符序列,然后插入到MI-泛化后缀树中,避免了穷举各种组合,从而快速索引全部聚类结果.实验结果显示MI-TSB算法具有良好的运行性能,成功聚类出非线性相关的对象;利用Gene Ontology对聚类结果进行基因注释,也验证了聚类结果的生物学意义.

Biclustering Nonlinearly Correlated Time Series Gene Expression Data

The biclustering algorithms focus on clustering correlated patterns in sub-spaces. However, most of the biclustering algorithms nowadays address only the linearly correlated pattern or a certain linearly similar pattern, leaving the nonlinearly correlated patterns untouched, which are often hidden in a great many of real data sets. In this paper, a novel biclustering algorithm called MI-TSB is proposed to find and report all nonlinearly correlated patterns in time series gene expression data. It first deduc...