Open Science

Explore our open access publications, data and models

Open code

Code repository associated with the FedECA article.

A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.

A Vision Transformer Large pretrained with Dinov2 self-supervised method on PANCAN-XL.

A self-supervised learning model for histopathology trained with iBOT.

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huggingface.co/owkin

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Explore our repositories on GitHub

github.com/owkin

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Open projects

AI moonshot to accelerate open science in Glioblastoma

Open access on medical and scientific results

We are seeking innovative lab facilities for wet-lab validation of our GBM targets and computing partners to revolutionize Glioblastoma patient care.

Open-access publications and preprints

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Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery

CytoSyn: a Foundation Diffusion Model for Histopathology - Tech Report

Thomas Duboudin

Multi-modal benchmarking of the Ultima UG100 and Illumina NovaSeq sequencing platforms using clinically relevant FFPE tissues

Spatially Integrated Multi-Omics reveals the Multicellular Landscape of Progenitor-Driven Glioblastoma Progression

Korbinian Traeuble

OKN4395, a first-in-class EP2/EP4/DP1 triple antagonist reprograms prostanoid-driven immunosuppression to restore antitumor immunity

Maximilien Grandclaudon

IMILIA: interpretable multiple instance learning for inflammation prediction in IBD from H&E whole slide images

Thalyssa Baiocco-rodrigues

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

Benoit Schmauch

Nature Communications

Prescription of off-label medications in patients on dialysis: time to challenge contraindications?

Clinical Kidney Journal

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

Multimodal prediction of metastatic relapse using federated deep learning in soft-tissue sarcoma with a complex genomic profile

Charles Maussion

Scientific Reports

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

Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma