Article ; Online: Deep Learning from Phylogenies for Diversification Analyses.
2023 Volume 72, Issue 6, Page(s) 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 term(s) | Animals ; Phylogeny ; Likelihood Functions ; Deep Learning ; Genetic Speciation ; Primates |
| Language | English |
| Publishing date | 2023-08-09 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 1482572-7 |
| ISSN | 1076-836X ; 1063-5157 |
| ISSN (online) | 1076-836X |
| ISSN | 1063-5157 |
| DOI | 10.1093/sysbio/syad044 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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