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首页> 外文期刊>Bioinformatics >Multiple gene expression profile alignment for microarray time-series data clustering.
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Multiple gene expression profile alignment for microarray time-series data clustering.

机译：多基因表达谱比对，用于微阵列时间序列数据聚类。

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MOTIVATION: Clustering gene expression data given in terms of time-series is a challenging problem that imposes its own particular constraints. Traditional clustering methods based on conventional similarity measures are not always suitable for clustering time-series data. A few methods have been proposed recently for clustering microarray time-series, which take the temporal dimension of the data into account. The inherent principle behind these methods is to either define a similarity measure appropriate for temporal expression data, or pre-process the data in such a way that the temporal relationships between and within the time-series are considered during the subsequent clustering phase. RESULTS: We introduce pairwise gene expression profile alignment, which vertically shifts two profiles in such a way that the area between their corresponding curves is minimal. Based on the pairwise alignment operation, we define a new distance function that is appropriate for time-series profiles. We also introduce a new clustering method that involves multiple expression profile alignment, which generalizes pairwise alignment to a set of profiles. Extensive experiments on well-known datasets yield encouraging results of at least 80% classification accuracy.
机译：动机：按时间序列对基因表达数据进行聚类是一个具有挑战性的问题，存在其自身的特殊约束。基于常规相似性度量的传统聚类方法并不总是适合于聚类时间序列数据。最近已经提出了一些用于对微阵列时间序列进行聚类的方法，该方法考虑了数据的时间维度。这些方法背后的固有原理是定义适合于时间表达数据的相似性度量，或者以在后续聚类阶段考虑时间序列之间和之内的时间关系的方式对数据进行预处理。结果：我们引入了成对基因表达谱比对，该比对可以垂直移动两个谱，以使它们对应曲线之间的面积最小。基于成对对齐操作，我们定义了一个适用于时间序列轮廓的新距离函数。我们还介绍了一种新的聚类方法，该方法涉及多个表达谱比对，将成对比对推广到一组谱。在众所周知的数据集上进行的广泛实验得出的分类结果至少达到80％令人鼓舞。

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