Manolo F. Perez

Integrative taxonomy using traits and genomic data for Species Delimitation with Deep learning

This paper introduces a deep learning method that integrates genomic and trait data to improve species delimitation in complex speciation scenarios.

PhyloCNN: Improving tree representation and neural network architecture for deep learning from trees in phylodynamics and diversification studies

PhyloCNN improves tree representation and neural network architecture for deep learning in phylodynamics and diversification studies, achieving high accuracy with smaller training sets.

On the use of generative models for evolutionary inference of malaria vectors from genomic data

This paper uses generative deep learning to infer the evolutionary history of malaria vectors in West Africa, finding that a migration model better explains population dynamics than a model without post-split migration.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

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.