Julia Haag

The paper quantifies and predicts multiple sequence alignment difficulty using AlDiScore, achieving high R2 scores on unseen datasets.

Pythia 2.0 improves upon its predecessor by using more data and a new model to better predict phylogenetic analysis difficulty, with increased accuracy and speed.

Pandora estimates the stability of dimensionality reduction in genotype data using bootstrapping and k-means clustering to assess uncertainty in population assignments.

The paper introduces EBG, a machine learning tool that predicts Felsenstein Bootstrap Support values for phylogenies, achieving high speed and accuracy compared to existing methods.

This study evaluates the realism of sequence evolution simulations by testing how well supervised learning can distinguish simulated from empirical MSAs.

The paper introduces an adaptive tree search heuristic in RAxML-NG that adjusts search thoroughness based on dataset difficulty, achieving significant speedups while maintaining accuracy.

The paper introduces Pythia, a Random Forest Regressor that predicts the difficulty of phylogenetic analyses under the Maximum Likelihood model.

The paper evaluates phylogenetic inference tools on simulated and empirical data, finding poor performance on difficult alignments and highlighting discrepancies between simulated and empirical accuracy.

This study shows that adjusting numerical thresholds in phylogenetic inference software can significantly speed up tree calculations without affecting tree quality.