预测细胞组成成分之间实体的或者功能性的连接通常依赖于实验测得数据（如基因表达量）的相关性。 但是仅从实验数据的相关性，我们无法区分其是来自于直接作用还是间接作用。Barabási 等人发明了一种方法，能够抑制间接作用的影响。他们利用观察到的相关数据然后用矩阵变换的方法将相关矩阵转变为一种显著突出直接连边的矩阵。他们用大肠杆菌调控作用数据对此方法进行了验证，发现其预测准确率比普通的相关方法高出了50%多，同时比互信息方法高出了6%。这种从实验相关数据直接读出系统相互作用的方法，在链路预测或者推断控制生物网络的动力学机制等方面，能够给人们带来一定的启示。

  Predictions of physical and functional links between cellular components are often based on correlations between experimental measurements, such as gene expression. However, correlations are affected by both direct and indirect paths, confounding our ability to identify true pairwise interactions. Here we exploit the fundamental properties of dynamical correlations in networks to develop a method to silence indirect effects. The method receives as input the observed correlations between node pairs and uses a matrix transformation to turn the correlation matrix into a highly discriminative silenced matrix, which enhances only the terms associated with direct causal links. Against empirical data for Escherichia coli regulatory interactions, the method enhanced the discriminative power of the correlations by twofold, yielding >50% predictive improvement over traditional correlation measures and 6% over mutual information. Overall this silencing method will help translate the abundant correlation data into insights about a system’s interactions, with applications ranging from link prediction to inferring the dynamical mechanisms governing biological networks.

研究成果：

Network link prediction by global silencing of indirect correlations

• Baruch Barzel
• & Albert-László Barabási

• Affiliations
• Contributions
• Corresponding author

 

Nature Biotechnology 

31 ,

720–725

(2013)

 

原文链接：

http://www.nature.com/nbt/journal/v31/n8/full/nbt.2601.html