Measuring the dissimilarity of a phylogenetic tree with respect to a reference tree or the hypotheses is a fundamental task in the phylogenetic study. A large number of methods have been proposed to compute the distance between the reference tree and the target tree. Due to the presence of unresolved relationships among the species, it is challenging to obtain a precise and an accurate reference tree for a selected dataset. As a result, the existing tree comparison methods may behave unexpectedly in various scenarios. In this paper, we introduce a novel scoring function, called the deformity index, to quantify the dissimilarity of a tree based on the list of clades of a reference tree. The strength of our proposed method is that it depends on the list of clades that can be acquired either from the reference tree or from the hypotheses. We investigate the distributions of different modules of the deformity index and perform different goodness-of-fit tests to understand the cumulative distribution. Then, we examine, in detail, the robustness as well as the scalability of our measure by performing different statistical tests under various models. Finally, we experiment on different biological datasets and show that our proposed scoring function overcomes the limitations of the conventional methods.

测量系统发育树相对于参考树或假设的差异是系统发育研究中的一项基本任务。已经提出了大量方法来计算参考树和目标树之间的距离。由于物种之间存在未解决的关系，因此很难为选定的数据集获得精确且准确的参考树。因此，现有的树比较方法可能在各种情况下表现出乎意料。在本文中，我们引入了一种新的评分函数，称为畸形指数，根据参考树的进化枝列表量化树的相异性。我们提出的方法的优势在于它取决于可以从参考树或假设中获取的进化枝列表。我们研究了畸形指数不同模块的分布，并进行了不同的拟合优度检验以了解累积分布。然后，我们通过在各种模型下执行不同的统计测试来详细检查我们度量的稳健性和可扩展性。最后，我们在不同的生物数据集上进行了实验，并表明我们提出的评分函数克服了传统方法的局限性。