Yunierkis Pérez‐Castillo

This study identifies essential genes in breast cancer using OncoOmics approaches, revealing 144 genes with 33% being cancer hallmarks and 14% significant across multiple methods.

The paper develops an artificial neural network model to predict breast cancer-related proteins, achieving high accuracy and identifying top-ranked proteins in three categories.

The paper develops a machine learning model to predict druggable proteins, achieving high accuracy, and identifies potential targets in cancer and RNA-binding proteins.

CompScore improves structure-based virtual screening by integrating docking scoring function components into consensus scoring, achieving higher enrichment across 102 targets.

ShadowCaster detects horizontal gene transfer events in prokaryotes by combining compositional features with phylogenetic models, achieving high accuracy in both artificial and bacterial genomes.