R. Betancur-R.

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.

This study uses phylogenomic data to resolve the evolutionary relationships and non-monophyly within piranha and pacu families, showing that convergent diets obscure traditional taxonomy.

The paper presents seven new exon probe sets and a bioinformatics pipeline for fish phylogenomics, enabling accurate phylogenetic analysis by filtering paralogs and integrating old and new data.