Fuzzy C-means method with empirical mode decomposition for clustering microarray data
Fuzzy C-means method with empirical mode decomposition for clustering microarray data
复制标题
用于聚类微阵列数据的经验模态分解的模糊 C 均值方法
DOI：
10.1109/bibm.2010.5706561
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发表时间：
2010-12
期刊：
影响因子：
--
通讯作者：
Vo Ang
中科院分区：
文献类型：
--
作者：
Yan-Fei Wang;喻祖国;Vo Ang
微阵列技术通过使并行监测数千个基因的表达成为可能，彻底改变了基因组研究。数据聚类分析已被广泛应用于从所获得的基因表达谱中提取信息。
Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. Existing clustering approaches, mainly developed in computer science, have been adapted to microarray data. Among these approaches, fuzzy C-means (FCM) method is an efficient one. However, microarray data contains noise and the noise would affect clustering results. Some clustering structure still can be found from random data without any biological significance. In this paper, we propose to combine the FCM method with the empirical mode decomposition (EMD) for clustering microarray data in order to reduce the effect of the noise. We call this method fuzzy C-means method with empirical mode decomposition (FCM-EMD). Using the FCM-EMD method on gene microarray data, we obtained better results than those using FCM only. The results suggest the clustering structures of denoised data are more reasonable and genes have tighter association with their clusters. Denoised gene data without any biological information contains no cluster structure. We find that we can avoid estimating the fuzzy parameter m in some degree by analyzing denoised microarray data. This makes clustering more efficient. Using the FCM-EMD method to analyze gene microarray data can save time and obtain more reasonable results.
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DOI：
--
发表时间：
2000
期刊：
Proceedings. International Conference on Intelligent Systems for Molecular Biology
影响因子：
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0.3
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影响因子：
16
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DOI：
10.1007/0-387-26288-1_2
发表时间：
2006
期刊：
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