DNA甲基化微阵列的非参数贝叶斯聚类算法

张林; 刘辉

doi:10.3724/SP.J.1004.2012.01709

DNA甲基化微阵列的非参数贝叶斯聚类算法

doi: 10.3724/SP.J.1004.2012.01709

张林,
刘辉

1.
中国矿业大学信息与电气工程学院徐州 221116

详细信息

通讯作者:
张林

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- 被引次数: 0
出版历程
- 收稿日期: 2011-08-22
- 修回日期: 2012-03-28
- 刊出日期: 2012-10-20

Nonparametric Bayesian Clustering Methods of DNA Methylation Microarray

ZHANG Lin,
LIU Hui

1.
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116

摘要

摘要: 面向 Illumina GoldenGate 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.
- DNA 甲基化微阵列 /
- Dirichlet 过程 /
- 贝塔混合模型 /
- 吉布斯抽样
Abstract: 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.
- DNA methylation microarray /
- Dirichlet process /
- beta mixture model /
- Gibbs sampling

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参考文献(1)

[1]

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