Miguel Pérez-enciso A

This study evaluates deep learning for predicting rice traits using structural and single-nucleotide genomic variants, finding that deep learning models outperform Bayesian models in 75% of cases.

This paper demonstrates generating realistic fruit shapes from DNA sequences using a decoder deep learning model in Cucurbitaceae and tomato.

This paper evaluates the utility of combining microbiome and genome data for complex trait prediction using simulation and variance-component analysis, finding that microbiome data improve prediction accuracy but causal effects are hard to identify.

SeqBreed evaluates genomic prediction in complex scenarios by simulating genetic data and GP methods, demonstrating its use with Drosophila and potato datasets.