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DNA甲基化微阵列的非参数贝叶斯聚类算法

Nonparametric Bayesian Clustering Methods of DNA Methylation Microarray
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摘要 面向Illumina Golden Gate甲基化微阵列数据提出了一种基于模型的聚类算法.算法通过建立贝塔无限混合模型,采用Dirichlet过程作为先验,实现了基于数据和模型的聚类结构的建立,实验结果表明该算法能够有效估计出聚类类别个数、每个聚类类别的混合权重、每个聚类类别的特征等信息,达到比较理想的聚类效果. A model based clustering algorithm for Illumina GoldenGate microarray data is proposed in this paper. By infinite beta mixture model and by adopting Dirichlet process as prior knowledge, the cluster structure can be defined based on model and data. Simulation results demonstrate that this methodology can estimate the number of clusters, the cluster mixing weight and the own characteristic of each cluster, and can reach relatively ideal clustering effect.
作者 张林 刘辉
机构地区 中国矿业大学信息与电气工程学院
出处 《自动化学报》 EI CSCD 北大核心 2012年第10期1709-1713,共5页 Acta Automatica Sinica
基金 中央高校业务专项基金(2010QNA50 2010QNA47)~~
关键词 DNA 甲基化微阵列 DIRICHLET 过程 贝塔混合模型 吉布斯抽样 DNA methylation microarray, Dirichlet process, beta mixture model, Gibbs sampling
分类号 Q523 [生物学—生物化学] TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献15

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二级参考文献109

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自动化学报
2012年 第10期

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