Humberto González‐Díaz

The paper identifies toxic intermediates in fungal metabolic pathways as potential antifungal targets using a random forest model and provides a web-service for exploration.

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.