Loïc Herpin

Identification of non-covalent inhibitors for the atypical peroxiredoxin PRDX5 as a therapeutic strategy in malignant pleural mesothelioma

The paper identifies non-covalent inhibitors of PRDX5, a protein upregulated in MPM, through a machine learning guided pipeline and biochemical validation, suggesting it as a therapeutic target.

A deep learning-based multiscale integration of spatial omics with tumor morphology.

This paper introduces MISO, a deep learning method that predicts spatial transcriptomics data from H&E slides, enabling spatially resolved gene expression and patient representation for downstream tasks.

Deciphering Cellular Ecosystems Driving Tumor Progression and Immune Escape from Spatial Transcriptomics and Single-Cell with COMPOTES

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.

Multiple instance learning with spatial transcriptomics for interpretable patient-level predictions: application in glioblastoma

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

Development of a Deep Learning model Tailored for HER2 Detection in Breast Cancer to aid pathologists in interpreting HER2-Low cases

This paper develops a deep learning model to detect HER2 status in breast cancer, showing higher concordance with ground truth than pathologists for HER2-low cases.