Carola Sophia Heinzel

The paper evaluates methods to classify individuals into populations using ancestry informative markers, finding a naive Bayes classifier performs best in identifying out-of-reference individuals.

The paper improves intra-European ancestry prediction by selecting informative genetic markers using machine learning, achieving higher accuracy than existing marker panels.

This paper benchmarks forensic ancestry prediction tools against TabPFN, showing TabPFN outperforms existing methods in accuracy and ROC AUC.

The paper shows that maximum likelihood estimates in the admixture model are non-unique and provides an algorithm to compute the set of equally likely estimates.