Artikel ; Online: Deep Learning from Phylogenies for Diversification Analyses.
2023 Band 72, Heft 6, Seite(n) 1262–1279
Abstract: Birth-death (BD) models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood ... ...
Abstract | Birth-death (BD) models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood formula must be established each time a new model is proposed; for some models, such a formula is not even tractable. Deep learning can bring solutions in such situations, as deep neural networks can be trained to learn the relation between simulations and parameter values as a regression problem. In this paper, we adapt a recently developed deep learning method from pathogen phylodynamics to the case of diversification inference, and we extend its applicability to the case of the inference of state-dependent diversification models from phylogenies associated with trait data. We demonstrate the accuracy and time efficiency of the approach for the time-constant homogeneous BD model and the Binary-State Speciation and Extinction model. Finally, we illustrate the use of the proposed inference machinery by reanalyzing a phylogeny of primates and their associated ecological role as seed dispersers. Deep learning inference provides at least the same accuracy as likelihood-based inference while being faster by several orders of magnitude, offering a promising new inference approach for the deployment of future models in the field. |
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Mesh-Begriff(e) | Animals ; Phylogeny ; Likelihood Functions ; Deep Learning ; Genetic Speciation ; Primates |
Sprache | Englisch |
Erscheinungsdatum | 2023-08-09 |
Erscheinungsland | England |
Dokumenttyp | Journal Article |
ZDB-ID | 1482572-7 |
ISSN | 1076-836X ; 1063-5157 |
ISSN (online) | 1076-836X |
ISSN | 1063-5157 |
DOI | 10.1093/sysbio/syad044 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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