Sophie Lèbre

The paper optimizes data integration for GRN inference in Arabidopsis thaliana by introducing DIOgene, which improves prediction accuracy and identifies gene-specific integration intensities.

This paper introduces TFscope, a machine learning method to identify genomic features explaining binding differences between TFs in ChIP-seq experiments, showing that co-factors and nucleotide environment often drive differences in cell types or treatments, while core motif differences are key for paralogous TFs.

The paper introduces a computational method to identify long regulatory elements across eukaryotic genomes, including Plasmodium falciparum, and shows their role in gene regulation.

The paper introduces TFcoop, a method to infer TF combinations involved in target TF binding, and shows it outperforms PWM methods while revealing cell-type-specific and promoter-class-specific TF interactions.

The paper develops a sequence-based method to predict gene expression accurately, showing that nucleotide composition and regulatory region contributions are key factors.