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Applications of a new subspace clustering algorithm (COSA) in medical systems biology

机译:一种新的子空间聚类算法(COSA)在医学系统生物学中的应用

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A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies.
机译:有人提出了一种新颖的聚类方法,即在属性子集上聚类对象(COSA)(Friedman and Meulman,(2004)。在属性子集上聚类对象。JRStatist。Soc。B 66,1-25。),用于对属性的子集进行无监督分析。复杂的数据集。我们证明了其在医学系统生物学研究中的有用性。描述了代谢组学分析的例子以及基于对大鼠和人类疾病病理学和干预作用的研究的无监督聚类。与基于欧几里得距离的主成分分析和层次聚类相比,COSA增强了追踪对象组中部分相似性的能力,从而为系统生物学提供了一种新的发现方法,并提供了一种揭示复杂多目标共同点的独特方法。动物和人类研究中的阶乘疾病。

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