Gabriel Peyré A

The paper introduces Xpressor, a framework for cross-scale learning in biology, improving cell-type prediction and embedding quality.

The paper introduces FLOWSATATE, a trajectory inference method using time-resolved RNA labeling data to model cell fate dynamics and predict future states.

CHAMPOLLION improves multi-omics integration by using regularized optimal transport to align unpaired cells and reveal cross-modal relationships, outperforming existing methods with fewer paired cells.

scPRINT-2 is a single-cell foundation model pre-trained on 350 million cells across 16 organisms, achieving state-of-the-art performance in expression denoising, cell embedding, and cell type prediction.

scPRINT pre-trains on 50 million cells to predict gene networks, outperforming existing methods in inference and zero-shot tasks.

scConfluence improves single-cell diagonal integration by combining autoencoders with regularized Inverse Optimal Transport, outperforming existing methods in three biological applications.

The paper introduces STORIES, a method for learning cell fate landscapes from spatial transcriptomics data, showing improved spatial coherence in neural regeneration and gliogenesis analyses.

Mowgli integrates paired single-cell multi-omics data using a combination of NMF and OT, improving clustering and interpretability, and is benchmarked against state-of-the-art methods.

This paper introduces Optimal Transport as a cell-cell similarity metric for single-cell omics data, showing improved clustering performance across multiple datasets.