The Alan Turing Institute – Roche strategic partnership

Generating insights into disease, patient, and outcome heterogeneity using advanced analytics

Project status



The Turing's health and medical sciences programme has long-term interests in personalised healthcare and treatment heterogeneity – the analysis of how medical treatments can affect different people in different ways. The programme is seeking to generate insights which improve the understanding of patient and disease heterogeneity and its relevance to clinical outcomes.

The goal of this partnership is to establish a world-leading collaboration in advanced analytics between Roche and the Turing, focused on enabling the transformative benefits of personalised healthcare to become a reality for patients around the world. Publication of methods and algorithms will follow the principles of open science to ensure that they are reproducible and interoperable. 

Read the news story: The Alan Turing Institute launches a strategic partnership with Roche to generate insights into disease, patient, and outcome heterogeneity using advanced analytics

About Roche

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This expanded strategic partnership was launched following a successful Data Study Group at the Turing in April 2019, and a 12-week research project later the same year, explored in the case study Turing and Roche: Towards tailor-made lung cancer treatment.

More information on the partnership can be found in this short video from our event hosted in July 2021. 

Current projects

The Turing-Roche partnership is running for five years and will cover multiple activities with the 'North Star' of developing new data science methods to investigate large, complex, clinical and healthcare datasets to better understand how and why patients respond differently to treatment, and how treatment can be improved. Current partnership projects are listed below.

Structured missingness in heterogeneous data

Missing data are a common problem that arise in many fields and can significantly complicate analysis. While there are established ways to handle data that is missing at random, it is often the case that missing readings are structured in some way. Dealing with such structured missingness is substantially more challenging yet occurs commonly in healthcare (and other) contexts. Consequently, there is a need to develop rigorous ways to understand structured missingness and develop tools to handle it appropriately. 

In November 2021 we ran a series of virtual workshops on the topic of structured missingness, with the aim of convening a community of researchers interested in developing new tools and methods, to learn from heterogenous data with structured patterns of missing readings. You can read more about the workshops in the report in the downloads section below. We then ran a competitive funding call in this area to complement the workshops and fund projects in this area. We funded two 18-month projects to Professor Ginestra Bianconi and Dr Robin Mitra and their teams which you can read more about here. These projects will be starting in September 2022 and we will be sharing regular updates from the projects here and also through our Slack Workspace and Knowledge Share events, as well as open publications of methods, algorithms and results.

An blog about our partnership and this area of research was published on Genomics England's site Mind the Gap: Stories of Health Data Equity. The site publishes stories on different ideas, initiatives and perspectives to showcase the challenges of data diversity. You can check out our piece here.

Furthermore, the partnership also has published a perspectives piece on structured missingness in Nature Machine Intelligence. A product of the collaborative workshops mentioned above, the publication explores challenges that address and mitigate effects of structured missingness and where current research could go from here- that as well as creating tools to tackle structured missingness, gaining a more holistic understanding of it could convey important information about the data itself. You can find the publication here.

Predictive modelling

We are exploring the research theme of predictive modelling in healthcare and how this branch of advanced analytics can potentially predict and help improve patient outcomes and beyond. In October 2022 we ran a collaborative workshop with researchers to explore this theme which you can read about downloads section. We also ran an associated funding call and awarded funding for an 18-month project to Dr Brieuc Lehmann and his team- read more about this here.

Opportunities and engagement 

Knowledge share series

Our knowledge share series aims to bring together members of both Roche and Turing's networks and the wider scientific community to showcase partnership updates and research, knowledge share and hear different academic and industry perspectives to gain insight and help build new connections and collaborations. Find our monthly events and how to register here.


Sign up to The Alan Turing Institute-Roche strategic partnership mailing list to receive regular updates from the strategic partnership team and community. Mailings include information about Turing-Roche projects and opportunities, jobs, events and funding calls from both The Alan Turing Institute and Roche. If the link above does not work for you (i.e blocked by Roche's firewall) then please sign up via this form.

Turing-Roche Slack Workspace

Request to join our Slack Workspace to connect with the partnership team and the wider community.


Professor Ben MacArthur

Director of AI for Science and Government, Deputy Programme Director for Health and Medical Sciences, and Turing Fellow

Ryan Copping

Global Head of Data Science Acceleration, Product Development, Roche

Researchers and collaborators

Dr Robin Mitra

Research Theme Lead in Structured Missingness, Turing-Roche Partnership

Contact info

If you have any questions about the Turing-Roche partnership please contact [email protected] or Vicky Hellon, Community Manager for the partnership at [email protected].



Structured Missingness Workshops Report 2021

Predictive Modelling Workshop Report 2022