Benoît Playe

This paper proposes a method to reduce false positive drug target predictions by balancing negative examples in training data, improving prediction accuracy and reducing experimental costs.

This study evaluates deep learning architectures and data augmentation methods for chemogenomics to predict drug-target interactions at the proteome level.

The paper develops NN-MT, a multi-task SVM algorithm for predicting drug-protein interactions, showing efficient performance in proteome-wide and orphan case predictions.