Abstract

Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that a neural network can distinguish between four-taxon alignments that were evolved under conditions susceptible to either long-branch attraction or long-branch repulsion. When likelihood and parsimony methods are discordant, the neural network can provide insight as to which tree reconstruction method is best suited to the alignment. When applied to the contentious case of Strepsiptera evolution, our method shows robust support for the current scientific view, that is, it places Strepsiptera with beetles, distant from flies.

中文翻译：

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使用神经网络区分 Felsenstein 区域和 Farris 区域。

 抽象的

最大似然法和最大简约法是系统发育树重建的两种关键方法。在某些条件下，这两种方法中的每一种都可以或多或少地有效执行，从而导致未解决或有争议的系统发育。我们证明，神经网络可以区分在容易受到长分支吸引或长分支排斥的条件下进化的四分类单元排列。当似然法和简约方法不一致时，神经网络可以提供关于哪种树重建方法最适合对齐的见解。当应用于有争议的链翅目进化案例时，我们的方法显示了对当前科学观点的有力支持，即它将链翅目与甲虫放在一起，远离苍蝇。