Deep learning approaches to the phylogenetic placement of extinct pollen morphotypes.

ABSTRACT: The phylogenetic interpretation of pollen morphology is limited by our inability to recognize the evolutionary history embedded in pollen features. Deep learning offers tools for connecting morphology to phylogeny. Using neural networks, we developed an explicitly phylogenetic toolkit for analyzing the overall shape, internal structure, and texture of a pollen grain. Our analysis pipeline determines whether testing specimens are from known species based on uncertainty estimates. Features from specimens with uncertain taxonomy are passed to a multilayer perceptron network trained to transform these features into predicted phylogenetic distances from known taxa. We used these predicted distances to place specimens in a phylogeny using Bayesian inference. We trained and evaluated our models u

SUBMITTER: Adaime ME 

PROVIDER: S-EPMC10777098 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature