Ugnė Stolz

Bayesian neural networks enable inference of complex phylodynamic processes

This paper introduces BELLA, a Bayesian neural network framework for modeling complex phylodynamic processes, showing improved accuracy over linear models in capturing nonlinear relationships between predictors and evolutionary rates.

Single-cell phylodynamics reveal rapid late-stage colorectal cancer expansions

The paper uses single-cell whole-genome sequencing and Bayesian phylodynamic modeling to show colorectal tumors often originate decades before diagnosis and expand rapidly in the last two years.

Enhancing Evolutionary Timelines: The Impact of Stratigraphic Range Information on Phylogenetic Inference

This paper introduces the SRFBD model to improve phylogenetic inference by incorporating stratigraphic range data, showing it produces older divergence times and higher sampling proportions compared to the FBD model.

Integrating Transmission Dynamics and Pathogen Evolution Through a Bayesian Approach

This paper introduces the Transmission Tree (TnT) model to improve understanding of epidemic dynamics by integrating pathogen gene sequences and clinical data through a Bayesian approach.