Pascal Frossard B

Fine-Grained and Reliable Cell Classification from Pathology Images

CellBridge improves cell classification in pathology images by transferring molecular information, achieving higher accuracy on fine-grained labels.

Resolving context-specific protein-protein interactomes forbiological discovery and therapeutic target prioritisation

ProtScape learns context-specific protein interactomes across 200 cell types, improving interaction reconstruction and identifying disease-related mechanisms.

Topology-aware reconstruction of cellular state landscapes from microscopy using self-supervised learning

The paper introduces SI-SimCLR, a self-supervised learning framework for reconstructing cellular phenotypic landscapes from microscopy data without segmentation or annotation, revealing substate organization and connections in astrocytes.

Extended pre-training of histopathology foundation models uncovers co-existing breast cancer archetypes characterized by RNA splicing or TGF-β dysregulation

This paper extends pre-training of histopathology foundation models to specialize them for tumor tissue, revealing two coexisting breast cancer archetypes defined by RNA splicing and TGF-β dysregulation.

Tissue Reconstruction with Generative AI

LUNA reconstructs tissue architecture from dissociated single-cell RNA-seq data using spatial priors learned from spatial transcriptomics, validating on mouse brain datasets and disease models.

Dynamics of Functional Network Organization Through Graph Mixture Learning

This paper introduces a data-driven method to infer functional brain networks from task and resting state fMRI data, finding that the inferred networks closely resemble structural connectomes better than traditional methods.