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

Benoit Schmauch

Robust Evaluation of Deep Learning-based Representation Methods for Survival and Gene Essentiality Prediction on Bulk RNA-seq Data

Nature Scientific Reports

Subgroup analysis methods for time-to-event outcomes in heterogeneous randomized controlled trials

Valentine Perrin

Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery

Benoit Schmauch

Journal of Pathology Informatics

Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides

Charlie Saillard

Nature Communications

PyDESeq2: a python package for bulk RNA-seq differential expression analysis

Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling

Alexandre Filiot

Deep learning predicts patients outcome and mutations from digitized histology slides in gastrointestinal stromal tumor

NPJ Precision Oncology

Adjuvant endocrine therapy uptake, toxicity, quality of life, and prediction of early discontinuation

Journal of the National Cancer Institute

A benchmark of computational pipelines for single-cell histone modification data

Félix Raimundo

SRATTA : Sample Re-ATTribution Attack of secure aggregation in federated learning

Tanguy Marchand

Proceedings of Machine Learning Research

Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma

Charlie Saillard

Nature Communications