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Multi-modal modeling in precision medicine: from data imputation to synthetic data​

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  • UserDr. Olivier Gevaert​ -​ Associate Professor, Depts of Medicine & Biomedical Data Science, Stanford University​ ​
  • ClockMonday 15 September 2025, 13:00-14:00
  • HouseCRUK CI Lecture Theatre.

If you have a question about this talk, please contact Simona Valeviciute .

Missing data presents a persistent challenge in biomedical research. Data imputation techniques have evolved from single-modality approaches to multi-modal approaches, which show great promise for imputing one modality based on the availability of another. Recent advancements in large, pre-trained artificial intelligence (AI) models, known as foundation models, offer even more powerful solutions for data imputation. We introduce the concept of cross-modal data modeling, a methodology harnessing foundation models to impute missing data and also generate realistic synthetic samples. Multi-modal modeling empowers researchers to model complex interactions among diverse biomedical data types, including omics and imaging. This approach can illuminate how one modality influences another, facilitating in-silico exploration of disease mechanisms without the need for extensive and costly real-world data collection. We highlight ongoing efforts in multi-modal modeling in spatial omics, digital pathology and radiology, and anticipate its substantial contributions to understanding disease biology and enhancing healthcare practices.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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FW 11, William Gates Building. Zoom link: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon Room 009, Cancer Research UK Cambridge Research Institute ONLINE & @ FW11 Seminar Room (William Gates Building)

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