A Comparison of Deep Learning Architectures for Inferring Parameters of Diversification Models from Extant Phylogenies
I. Lajaaiti, S. Lambert, J. Voznica, H. Morlon, F. Hartig
This study compares deep learning architectures for inferring diversification model parameters from phylogenies, finding that CNNs and RNNs with LTTs outperform other methods for CRBD, while BISSE requires topology and tip states.
bioRxiv · Mar 3, 2023 · PreprintEcology