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Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices
2020
BMC Genomics
With the rapid growth rate of newly sequenced genomes, species tree inference from genes sampled throughout the whole genome has become a basic task in comparative and evolutionary biology. However, substantial challenges remain in leveraging these large scale molecular data. One of the foremost challenges is to develop efficient methods that can handle missing data. Popular distance-based methods, such as NJ (neighbor joining) and UPGMA (unweighted pair group method with arithmetic mean)
doi:10.1186/s12864-020-06892-5
pmid:32689946
fatcat:xw5uelfpgzft3jroeu4zbrhq7a