Early disease risk prediction from longitudinal clinical records using AI

Monday, 6 July 2026, 9am to 10am

For the BDI Distinguished Seminar, we will be hearing from Chris Sander, Director of CBio Center and Professor of Cell Biology, Harvard Medical School. We’re delighted to host Chris in what promises to be a great talk!

Date: Monday 6 July
Time: 9:00 am – 10:00 am
Talk title: Early disease risk prediction from longitudinal clinical records using AI
Location: Big Data Institute, Seminar Room 0

Abstract
The Sander Lab, with postdocs Asif Khan, Duncan Forster and Moshir Harsh, and with collaborators Chunlei Zheng and Nathaneal Fillmore at the US-VA, Daniel Ritter at Harvard FAS, Qi Wei and Jenn Hadlock at the Institute of Systems Biology in Seattle with Tanya Sorensen at Providence Health, and Erica Warner, Allison Chang and Lecia Sequist at Mass General Hospital are tackling the challenge of combining health state foundation models with cancer-case supervised fine-tuning to predict cancer occurrence in specific time intervals. The models are trained on millions or real-world patient health trajectories and support the design and implementation of disease interception programs (early detection and prevention). This benefits patients and reduces nation-wide health care costs. Test those at highest risk to catch cancer early.

Short biography
Chris Sander is a pioneer in biological data science, with recent innovation in computational cell biology and cancer risk stratification. He is faculty in Systems Biology at Harvard Medical School. He previously founded the Biocomputing Program at EMBL, the science program at EMBL-EBI, the Computational Biology Program at MSKCC and the cBio Center at Dana-Farber Cancer Institute.
 
Sander made significant contributions to protein folding, functional genomics, cancer biology, and AI for biological problems. He was a leader in The Cancer Genome Atlas (TCGA) project and his group created the cBioPortal for Cancer Genomics and the Pathway Commons knowledge base. His current focus is on perturbation biology for the design of anti-resistance cancer therapeutics; and the prediction of cancer occurrence in specific time intervals using AI foundation models trained on millions or real-world patient health trajectories for the design of early-cancer interception (early detection and prevention) programs.

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All members of the University are welcome to join, please let reception at BDI know you’re here for the seminar and sign-in. We hope you can join us!

Hybrid Option: Please note that these meetings are closed meetings and only open to members of the University of Oxford to encourage sharing of new and unpublished data. Please respect our speakers and do not share the link with anyone outside of the university.

Microsoft Teams meeting
Join: https://teams.microsoft.com/[…]/325775227448167?p=usb3F9r24a65W2IEDr
Meeting ID: 325 775 227 448 167
Passcode: wz9z9gS6

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Speaker(s): Chris Sander (Harvard Medical School)

Venue: Big Data Institute - Lower Ground Seminar Room 0 - Lower Ground Seminar Room 0 Big Data Institute Old Road Campus Oxford Oxfordshire OX3 7LF United Kingdom

Department: Big Data Institute - NDPH (Unit)

Organiser: Sumeeta Maheshwari

Host: Prof Trey Ideker