Foundation models can be advantageously harnessed to estimate missing data in multimodal biomedical datasets and to generate realistic synthetic samples.
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Acknowledgements
The research reported here was supported by the National Cancer Institute (NCI; grant number R01 CA260271). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. M.P. was supported by a grant from Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO; 1161223N and V467423N. The work was further supported by grants from FWO (3G045620, 3G046318) and UGent BOF (BOF01J06219, BOF/IOP/2022/045BOF).
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F.C.-P. and M.P. prepared the figure. F.C.-P., M.P. and O.G. conceived the idea and wrote the paper. O.G. and K.M. reviewed the paper and obtained the funding. All authors reviewed and accepted the submitted version of the paper.
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Carrillo-Perez, F., Pizurica, M., Marchal, K. et al. Synthetic multimodal data modelling for data imputation. Nat. Biomed. Eng 9, 421–425 (2025). https://doi.org/10.1038/s41551-024-01324-1
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DOI: https://doi.org/10.1038/s41551-024-01324-1
