We show that our model can effectively predict the presence/absence of discordance, estimate the probability of discordance, and infer the correct species tree topology in the presence of multiple, common sources of error. Here, rather than applying machine learning to the problem of inferring single tree topologies, we develop a model to infer important properties of a particular internal branch of the species tree via genome-scale summary statistics extracted from individual alignments and inferred gene trees. ![]() Among distantly related taxa, however, it is difficult to differentiate these biological sources of discordance from discordance due to errors in gene tree reconstruction, even when supervised machine learning techniques are used to infer individual gene trees. Gene tree discordance due to incomplete lineage sorting or introgression has been described in numerous genomic datasets.
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