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Mechanism-Informed Multimodal Generative AI for Causal and Dynamical Modelling in Biomedical Research

CORDIS · observation · Publication date unknown

Mechanism-Informed Multimodal Generative AI for Causal and Dynamical Modelling in Biomedical Research AIRIS will develop the next generation of multimodal Generative AI (GenAI) models to accelerate research on predictive and personalised medicine. Building on recent advances in LLMs, causal inference and mechanistic modelling, AIRIS will integrate heterogeneous biomedical data (omics, imaging, clinical, laboratory, lifestyle, PROMs and scientific literature) into mechanism-informed generative frameworks. These models will not only generate biologically plausible synthetic data to address sparsity and bias but also embed causal and dynamical constraints across biological scales, from “virtual cells” to multi-organ models, enabling counterfactual reasoning and in-silico hypothesis testing. The project will deliver (i) robust agentic multimodal data integration pipelines; (ii) novel mechanism-anchored generative archite

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recordType

award

status

SIGNED

region

EU

value

16944728.5

unit

EUR