A special local clustering algorithm for identifying the genes associated with Alzheimer's disease.
A special local clustering algorithm for identifying the genes associated with Alzheimer's disease.
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DOI：
10.1109/tnb.2009.2037745
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发表时间：
2010-03
影响因子：
3.9
通讯作者：
Huang X
中科院分区：
文献类型：
--
作者：
Pang CY;Hu W;Hu BQ;Shi Y;Vanderburg CR;Rogers JT;Huang X
聚类是将相似的对象分组到一个类中。局部聚类特征是指一组数据与另一组数据分离，并且来自这些不同组的数据被局部聚类的现象。紧凑类被定义为一个集群，其中所有相似的元素都紧密地聚集在集群内。在这里，本地聚类功能的本质，揭示了数学运算，结果在一个新的聚类算法被称为特殊的本地聚类（SLC）算法，用于处理与阿尔茨海默病（AD）相关的基因微阵列数据。SLC算法能够将具有相似表达模式的基因分组在一起，并将显著变化的基因表达值识别为孤立点。如果某个基因属于对照数据中的紧凑类，并且在早期、中度和/或重度AD基因微阵列数据中作为孤立点出现，则该基因可能与AD相关。聚类算法在疾病相关基因识别中的应用，如在AD中的报道很少。
Clustering is the grouping of similar objects into a class. Local clustering feature refers to the phenomenon whereby one group of data is separated from another, and the data from these different groups are clustered locally. A compact class is defined as one cluster in which all similar elements cluster tightly within the cluster. Herein, the essence of the local clustering feature, revealed by mathematical manipulation, results in a novel clustering algorithm termed as the special local clustering (SLC) algorithm that was used to process gene microarray data related to Alzheimer’s disease (AD). SLC algorithm was able to group together genes with similar expression patterns and identify significantly varied gene expression values as isolated points. If a gene belongs to a compact class in control data and appears as an isolated point in incipient, moderate and/or severe AD gene microarray data, this gene is possibly associated with AD. Application of a clustering algorithm in disease-associated gene identification such as in AD is rarely reported.