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SUMMARY:Multimodal data fusion for precision medicine& digital twins - Ol
 ivier Gevaert (Stanford University)
DTSTART:20250919T111000Z
DTEND:20250919T113000Z
UID:TALK235453@talks.cam.ac.uk
DESCRIPTION:Missing data presents a persistent challenge in biomedical res
 earch. Data imputation techniques have evolved from single-modality approa
 ches to multi-modal approaches\, which show great promise for imputing one
  modality based on the availability of another. Recent advancements in lar
 ge\, pre-trained artificial intelligence (AI) models\, known as foundation
  models\, offer even more powerful solutions for data imputation. We intro
 duce the concept of cross-modal data modeling\, a methodology harnessing f
 oundation models to impute missing data and also generate realistic synthe
 tic samples. Multi-modal modeling empowers researchers to model complex in
 teractions among diverse biomedical data types\, including omics and imagi
 ng. This approach can illuminate how one modality influences another\, fac
 ilitating in-silico exploration of disease mechanisms without the need for
  extensive and costly real-world data collection. We highlight ongoing eff
 orts in multi-modal modeling in spatial omics\, digital pathology and radi
 ology\, and anticipate its substantial contributions to understanding dise
 ase biology and enhancing healthcare practices.&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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