The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI)
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …

Deep learning has led to incredible breakthroughs in areas of research, from self-driving
vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however …

Vast amounts of pathogen genomic, demographic and spatial data are transforming our
understanding of SARS-CoV-2 emergence and spread. We examined the drivers of …

Phylogenetic trees are believed to contain a wealth of information on diversification
processes. However, comparing phylogenetic trees is not straightforward due to their high …

Phylodynamics is central to understanding infectious disease dynamics through the
integration of genomic and epidemiological data. Despite advancements, including the …

Abstract Analysis of phylogenetic trees has become an essential tool in epidemiology.
Likelihood-based methods fit models to phylogenies to draw inferences about the …

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human
society. Beginning with genome surveillance of severe acute respiratory syndrome …

Abstract Machine learning has increasingly been applied to a wide range of questions in
phylogenetic inference. Supervised machine learning approaches that rely on simulated …

Birth–death (BD) models are widely used in combination with species phylogenies to study
past diversification dynamics. Current inference approaches typically rely on likelihood …

Motivation The application of machine learning approaches in phylogenetics has been
impeded by the vast model space associated with inference. Supervised machine learning …