A simple and exact Laplacian clustering of complex networking phenomena: Application to gene expression profiles
A simple and exact Laplacian clustering of complex networking phenomena: Application to gene expression profiles
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DOI：
10.1073/pnas.0708598105
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发表时间：
2008-03-18
影响因子：
11.1
通讯作者：
Chang, Iksoo
中科院分区：
文献类型：
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作者：
Kim, Choongrak;Cheon, Mookyung;Chang, Iksoo
揭开复杂网络现象的统一网络特征是一项令人感兴趣但艰巨的任务。目前还没有一个具有严格框架的简单战略。利用统计物理中主方程转移矩阵的精确代数性质，我们提出了一种基于拉普拉斯矩阵的方法，用于在每个样本的类别完全未知的无监督复杂网络现象中发现和预测新的类别。使用这种拉普拉斯方法，我们可以同时发现不同的类并确定每一类的身份。通过对应用于基因表达谱、白血病数据的真实数据集的拉普拉斯方法的说明性测试[Golub tr，et al.(11999)，以及淋巴瘤数据[Alizadeh AA，et al.(2000)Natural 403：503-511]，我们通过数学和物理实现证明了这种方法是准确和健壮的。它提供了一个总体框架，用于描述广泛领域中任何类型的复杂网络现象，无论它们是受监督的还是无监督的。
Unraveling of the unified networking characteristics of complex networking phenomena is of great interest yet a formidable task. There is currently no simple strategy with a rigorous framework. Using an analogy to the exact algebraic property for a transition matrix of a master equation in statistical physics, we propose a method based on a Laplacian matrix for the discovery and prediction of new classes in the unsupervised complex networking phenomena where the class of each sample is completely unknown. Using this proposed Laplacian approach, we can simultaneously discover different classes and determine the identity of each class. Through an illustrative test of the Laplacian approach applied to real datasets of gene expression profiles, leukemia data [Golub TR, et al. (11999) Science 286:531-537], and lymphoma data [Alizadeh AA, et al. (2000) Nature 403:503-511], we demonstrate that this approach is accurate and robust with a mathematical and physical realization. It offers a general framework for characterizing any kind of complex networking phenomenon in broad areas irrespective of whether they are supervised or unsupervised.