Jhonathan Pedroso Rigal dos Santos

mdBIRCH clusters molecular dynamics trajectories efficiently using an online method with a RMSD-calibrated merge test, enabling scalable and interpretable analysis.

DIVINE provides a deterministic clustering framework for molecular dynamics trajectories, achieving better clustering quality and efficiency than bisecting k-means.

This paper introduces HELM, a hybrid clustering method combining k-means efficiency with hierarchical flexibility, and demonstrates its application to protein-DNA and protein folding simulations.

The paper uses Bayesian probability methods to analyze genotype-by-environment interactions in plant breeding, improving cultivar recommendation by quantifying adaptation and stability probabilities.

This paper develops Bayesian networks for genomic prediction of developmental traits in biomass sorghum, showing improved accuracy for plant height and biomass yield using multi-time and multi-trait models.

This study compares deleterious mutation patterns between sorghum and maize, finding sorghum diverges from the domestication cost hypothesis and develops a gene deleterious index with accuracy over 0.5.