Dr Alicia González Díaz

Omics and artificial intelligence (AI) are reshaping our understanding of Alzheimer’s disease (AD). Through these powerful technologies, we can map genes, gene expression, and proteins in every cell of AD brains. Moreover, all this molecular information can be correlated with patient-specific clinical and neuropathological features through AI multimodal data integration, revealing that Alzheimer's is not a single disease but a complex, heterogeneous spectrum of disorders. Importantly, emerging research demonstrates that capturing this diversity in human cellular preclinical models is essential for developing treatments that actually work in patients. We can now use AI to predict what are the molecular differences between each patient subtype. However, while AI can rapidly predict hundreds of patient-specific "molecular signatures", we can only validate few using available biological approaches. This mismatch severely limits the translational power of AI tools. My postdoctoral research aims to solve this critical gap by building platforms and tools for quick, cost-effective integration of AI-predicted patient-specific disease fingerprints into human stem cells. This will enable scalable validation and model development, creating a "lab in the loop" approach that may ultimately enhance the predictive accuracy and translational power of AI pipelines for developing patient-tailored effective AD preclinical models and therapeutics.