Didier Barradas‐Bautista

Improving classification of correct and incorrect protein-protein docking models by augmenting the training set

The paper improves protein-protein docking model classification by augmenting the training set with a weakly supervised method, achieving higher accuracy and MCC than existing scoring functions.

A Random Forest Classifier for Protein-Protein Docking Models

The paper develops a Random Forest classifier, CoDES, to distinguish correct from incorrect protein-protein docking models using machine learning and scoring functions.

Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.