Efficiently Mining Time-Delayed Gene Expression Patterns
Efficiently Mining Time-Delayed Gene Expression Patterns
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DOI：
10.1109/tsmcb.2009.2025564
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发表时间：
2010-04
期刊：
影响因子：
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通讯作者：
Guoren Wang;Linjun Yin;Yuhai Zhao;Keming Mao
中科院分区：
文献类型：
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作者：
Guoren Wang;Linjun Yin;Yuhai Zhao;Keming Mao
与基于模式的双聚类方法不同，该方法专注于将对象分组在相同的维度子集中，在本文中，我们提出了一种新的时间序列基因表达数据的相干聚类模型，即，时延簇（TD-cluster）。在此模型下，如果对象遵循一定的时间延迟关系，则这些对象在不同的维度子集中可以是连贯的。这样的聚类可以发现基因表达的周期时间，这对于揭示基因调控网络至关重要。本文是首次尝试从微阵列数据中挖掘时间延迟的基因表达模式。一个新的算法也提出并实现挖掘所有重要的td-集群。我们的实验结果表明：1）td聚类算法可以检测到大量的聚类，这些聚类是以前的模型所遗漏的，并且这些聚类具有潜在的高生物学意义; 2）td聚类模型和算法可以很容易地扩展到3-D基因×样本×时间数据集来识别3-D td聚类。
Unlike pattern-based biclustering methods that focus on grouping objects in the same subset of dimensions, in this paper, we propose a novel model of coherent clustering for time-series gene expression data, i.e., time-delayed cluster (td-cluster). Under this model, objects can be coherent in different subsets of dimensions if these objects follow a certain time-delayed relationship. Such a cluster can discover the cycle time of gene expression, which is essential in revealing gene regulatory networks. This paper is the first attempt to mine time-delayed gene expression patterns from microarray data. A novel algorithm is also presented and implemented to mine all significant td-clusters. Our experimental results show following two results: 1) the td-cluster algorithm can detect a significant amount of clusters that were missed by previous models, and these clusters are potentially of high biological significance and 2) the td-cluster model and algorithm can easily be extended to 3-D gene × sample × time data sets to identify 3-D td-clusters.