Marie Piraud

This study develops a scalable neuromuscular organoid platform to analyze drug responses in spinal muscular atrophy, showing distinct muscle and neuronal responses to SMN2 splicing modulators.

This study analyzes lung epithelial cells across the lifespan in mice, identifying age-related declines in regeneration linked to inflammation and epigenetic changes.

The paper decodes stimulus identity and network state from calcium imaging in mice across different anesthesia states, showing preserved stimulus information even during deep anesthesia.

This study uses electrical stimulation to enhance the maturation of human neuromuscular organoids, improving their structural and functional properties.

DrugDiff generates novel small molecules with desired properties using a diffusion model guided by predictors.

This study links mHTTex1 aggregation in neurons to synaptic dysfunction and early mortality in HD flies through machine learning analysis and RNAi knockdown experiments.

Spapros selects optimal probe sets for targeted spatial transcriptomics to improve cell type identification and spatial variation detection across multiple datasets.

This paper introduces MouseMapper, a deep learning tool combined with 3D imaging to analyze whole-body structural changes in obesity, revealing nerve and immune system alterations.

The paper introduces MISATO, a dataset combining quantum mechanics properties and molecular dynamics simulations of protein-ligand complexes to support structure-based drug discovery.

DELiVR uses VR-generated data to train deep learning models for quantifying c-Fos in cleared mouse brains, revealing new activation patterns in cancer-related brain activity.

TopEC improves enzyme function classification using 3D graph neural networks and a localized 3D descriptor, achieving high accuracy in EC class prediction.

The paper develops a mixed-effect random forest model to predict CRISPRi guide efficiency using data from depletion screens and gene-specific features, outperforming existing methods.

This paper introduces a Bayesian framework for modeling tumor dissemination in Multiple Myeloma using longitudinal imaging data, showing improved prediction of disease progression compared to existing radiological criteria.

The paper develops a deep learning framework to analyze whole brain vasculature in cleared mouse brains, revealing new insights into vascular architecture and collateralization.