Ulises Rosas‐Puchuri

This paper introduces Qsin, a sparse learning method that reduces the size of concordance factors tables for phylogenetic network inference while maintaining accuracy and reducing computational time.

This paper introduces a non-linear phylogenetic regression method using regularized kernels, showing improved error reduction in both biological and simulated datasets.

This study uses machine learning to analyze phylogenetic datasets, finding that non-linear models outperform linear ones in capturing complex relationships affecting phylogenomic inference.