Ekaterina Noskova

GHIST 2024 evaluates population genetic inference methods through four demographic history challenges, revealing the dominance of site frequency spectrum-based methods and the potential of flexible models for complex histories.

The paper introduces a Bayesian optimization pipeline for demographic inference, showing it outperforms genetic algorithms for larger population counts.

The paper expands the stdpopsim species catalog and improves genome simulations by adding non-crossover recombination and species-specific annotations, making simulations more realistic for non-model organisms.

GADMA2 improves demographic inference by enhancing efficiency and flexibility in genetic data analysis.

The paper introduces Demes, a standard format for demographic models in population genetics to improve clarity and ease of use in simulations and analysis.