Simon Grouard

The paper introduces a machine-learning method to identify recurrent cell-cell communication patterns across tumor samples using spatial transcriptomics and single-cell data, revealing key immune and genetic programs in bladder cancer.

This paper introduces SpaMIL, a spatial transcriptomics-based multiple instance learning framework for patient-level outcome prediction in glioblastoma, achieving high prognostic accuracy.

The paper introduces MixupVI, a deep generative model for reference-free deconvolution of bulk RNA-seq data using single-cell transcriptomics, and releases PyDeconv for benchmarking deconvolution algorithms.

This study evaluates deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data, finding that non-DL methods perform comparably on survival tasks while DL methods excel in gene essentiality prediction.