Matthieu Najm

scROMA infers pathway activity from single-cell transcriptomics data, accounting for batch effects, and provides a simulation framework for benchmarking.

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

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.