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Prediction of Protein-protein interactions From Phylogenetic Trees Using Partial Correlation Coefficient

机译：使用部分相关系数预测来自系统发育树的蛋白质 - 蛋白质相互作用

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Computational prediction of protein-protein interactions from the sequence information is an important issue in bioinformatics. In recent years, the phylogenetic profile method [3] has been developed for predicting protein functions and discovering specific protein interactions, and the mirror tree method [2] has been proposed as a generalization of the idea of the phylogenetic profile in order to measure the evolutionary distance between proteins more precisely. Both methods basically stem from the assumption that functionally correlated proteins evolve in a correlated manner. In the mirror tree method, the intensity of the correlation between proteins is evaluated by Peason's correlation coefficient based on phylogenetic trees, but it has been pointed out that a number of false positives tend to be introduced in the prediction.
机译：从序列信息的蛋白质 - 蛋白质相互作用的计算预测是生物信息学中的重要问题。近年来，已经开发了用于预测蛋白质功能并发现特异性蛋白质相互作用的系统发育谱系[3]，并且已经提出了镜子树方法[2]作为系统发育剖面的概念的概括，以便测量蛋白质之间的进化距离更精确。两种方法基本上源于功能相关的蛋白质以相关方式发展的假设。在镜子树方法中，通过突出的基于系统发育树进行突出的相关系数来评估蛋白质之间的相关性的强度，但已经指出了在预测中倾向于引入许多误报。

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