Building a bridge between medical researchers & data scientists

Engineered for you, medical researchers, Owkin Studio is a software platform dedicated to finding new biomarkers, building prognosis models, and predicting response to treatment from multimodal patient data.

Embedded into Owkin’s unique federated learning and AI environment, Studio enables you to intuitively create and manage machine learning-based research projects, from cohort & project management to results interpretation

Unlike traditional black-box AI, Owkin Studio helps you to understand & interpret the results from a biological perspective. Collaborate hand-in-hand with our in-house scientists who will dedicate their expertise to your project.

Advance your research, publish your results, and join our mission to develop better drugs for patients. 

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Working with Owkin Studio

At Owkin, confidentiality and security of patient data matter above all. To respect this commitment, we developed and patented a unique set of technologies, served through Studio and fueled by state-of-the-art federated learning techniques.

Owkin Studio is deployed in your institution, on premise or in the cloud. The data stays behind your institution’s firewall and can be used to locally train machine learning models.

Take your project one step further and collaborate with Owkin Lab’s data scientists to design more advanced experiments or train additional algorithms, leading to the publication of relevant scientific results and medical discoveries in high impact journals.

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A ground breaking approach
How Owkin Studio accelerates your research

Intuitively set up AI projects

Build cohorts and specify medical hypotheses for your experiment. Train algorithms developed by our world class data scientists on your own research data. You do not have to be a machine learning expert yourself to use Studio – no coding is required.

Biologically interpret results

Because your research questions need biomedical answers. Look beyond traditional black-box AI: understand how the model analyzed your data and predicted results through Studio’s interpretability features. Integrate your medical knowledge to draw your own conclusions and refine your research.

Work with Owkin Lab

Studio is a collaborative platform where researchers, data scientists and biomedical experts come together. Propel your research by working hand-in-hand with Owkin Lab, and train new models that are tailor-made to your research question.


Malignant Mesothelioma – Studio Pathology

New biomarkers in malignant mesothelioma

In 2018 and 2019, Owkin collaborated with Francoise Galateau-Sallé, Department of Biopathology at Cancer Center Léon Bérard, to identify new histological features predictive of the survival of patients suffering from malignant mesothelioma.

Our model, MesoNet, trained on the Mesopath/Mesobank cohort, located unkown regions in the stroma that were associated with low patient survival. MesoNet is an example of how machine learning can provide tools and interpretation features to identify new biomarkers.

This new biomarker was published in Nature Medicine, and research is now continuing to identify new targets to deliver better treatments for malignant mesothelioma.

Discover how the model works, explore the cohort and the results, and analyze the tiles that display low and high survival scores.

Explore MesoNet live in Studio

COVID-19 – Studio Radiology

Understanding Covid-19 severity markers with our ScanCovIA model

In May 2020, Owkin developed a machine learning model in partnership with Gustave Roussy, Hôpital Kremlin-Bicêtre & INRIA to predict the severity of SARS-CoV-2 infection from initial CT-scans and clinical variables.

The preprint of our paper is available here. The results show that beyond AI modeling, a composite score integrating selected radiological measurements with relevant clinical and biological variables provides the most accurate predictions, and can rapidly become a reference for severity prediction.

The model is open sourced and can be visualized on public data in Owkin Studio.

Get in touch to try Studio Radio

HE2RNA – Studio Pathology

Predict RNA-seq expression of tumors from whole slide images

HE2RNA is a deep learning model built to predict RNA-seq expression of tumors from WSIs without the need for expert annotation. The model robustly and consistently predicted subsets of genes expressed in different cancer types, including genes involved in immune cell activation status and immune cell signaling.

HE2RNA is interpretable by design, and provides virtual spatialization of gene expression. Moreover, the transcriptomic representation learned by HE2RNA can be transferred to improve predictive performance for other tasks, particularly for small datasets.

This model was published by Nature Communications and has many high-impact applications, that range from direct evaluation of immune response to the augmentation of existing pathology cohorts with predicted gene expression.

Explore HE2RNA live in Studio

Work with us and augment your research project