加权网络测度在微阵列距离矩阵中的应用
Applying weighted network measures to microarray distance matrices
摘要：
在最近的工作中，我们提出了一种分析加权网络的新方法，通过提供定义在未加权网络上的任何网络测度的直接推广。这种方法是基于翻译的加权网络到一个合奏的边缘，特别适合于全连接的加权网络的分析。在这里，我们将我们的方法应用到几个这样的网络，包括距离矩阵，并表明聚类系数，通过使用集成方法构建，提供了有意义的见解系统研究。在特定的情况下，从微阵列实验的两个数据集的聚类系数确定了一些生物学意义的基因，优于现有的识别方法。
英文摘要：
In recent work we presented a new approach to the analysis of weighted networks, by providing a straightforward generalization of any network measure defined on unweighted networks. This approach is based on the translation of a weighted network into an ensemble of edges, and is particularly suited to the analysis of fully connected weighted networks. Here we apply our method to several such networks including distance matrices, and show that the clustering coefficient, constructed by using the ensemble approach, provides meaningful insights into the systems studied. In the particular case of two data sets from microarray experiments the clustering coefficient identifies a number of biologically significant genes, outperforming existing identification approaches.
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