Véronique Stoven

This study identifies proliferation and stress-adaptation mechanisms in MTUS1-low TNBC and proposes therapeutic strategies based on CRISPR-derived dependency signals.

The paper introduces IFPP, a molecular representation for identifying isofunctional molecules, and proposes Metric Learning to improve scalability for large libraries.

The paper constructs a CF signalling network from transcriptomic data to identify dysregulated proteins and potential therapeutic targets in Cystic Fibrosis.

The paper introduces Komet, a scalable DTI prediction method, and LCIdb, a large DTI dataset, showing improved scalability and performance over existing methods.

The paper introduces rROMA, a software package for computing gene set activity from omics data, and applies it to cystic fibrosis transcriptomic data to identify disease-related pathways and potential biases.

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.

LOTUS predicts cancer driver genes by integrating mutation and interaction data, outperforming existing methods across multiple cancer types.

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.

This paper introduces kernel multitask regression to predict compound toxicity on cell lines using chemical and genetic data, achieving strong performance on a benchmark challenge.