基于Ipsen-Mikhailov距离的生物网络比较
Biological network comparison via Ipsen-Mikhailov distance
摘要：
强调网络之间的相似性和差异是研究许多生物过程的一项信息丰富的任务。典型的例子是检测推断的网络和相应的金标准之间的差异，或者评估动态网络随时间的变化。虽然可以通过定性或基于特征的方法得出富有成效的见解，但在需要定量评估时必须使用距离。在这里，我们介绍了生物网络比较的Ipsen-Mikhailov度量，比较图的拉普拉斯特征值的分布的差异的基础上。作为一种谱度量，它的重点是网络的一般结构，因此它可以克服影响局部度量的问题，如编辑距离。与经典的马修斯相关系数（MCC）的关系进行了讨论，显示了更精细的鉴别分辨率实现的Ipsen-Mikhailov度量。我们的结论与三个例子中的应用功能基因组任务，包括网络重建的稳定性，数据二次采样的鲁棒性，动态网络的变异性和网络的差异与分类任务。
英文摘要：
Highlighting similarities and differences between networks is an informative task in investigating many biological processes. Typical examples are detecting differences between an inferred network and the corresponding gold standard, or evaluating changes in a dynamic network along time. Although fruitful insights can be drawn by qualitative or feature-based methods, a distance must be used whenever a quantitative assessment is required. Here we introduce the Ipsen-Mikhailov metric for biological network comparison, based on the difference of the distributions of the Laplacian eigenvalues of the compared graphs. Being a spectral measure, its focus is on the general structure of the net so it can overcome the issues affecting local metrics such as the edit distances. Relation with the classical Matthews Correlation Coefficient (MCC) is discussed, showing the finer discriminant resolution achieved by the Ipsen-Mikhailov metric. We conclude with three examples of application in functional genomic tasks, including stability of network reconstruction as robustness to data subsampling, variability in dynamical networks and differences in networks associated to a classification task.
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