AI-Guided Discovery of Blood-Based Biomarkers for Alzheimer’s Disease

New treatment options for Alzheimer’s disease are creating an urgent need for scalable, blood-based biomarkers that can enable earlier diagnosis and support more targeted clinical decisions beyond invasive testing and PET scans. Yet biomarker discovery remains a complex, iterative, and cross-functional process, requiring the integration of heterogeneous clinical and molecular datasets, continuous hypothesis testing, and rigorous validation.

In this joint case study with Washington University in St. Louis, we show how an agentic AI-guided workflow, embedded directly within the Genedata Profiler analytics environment, helps scientists move more quickly from complex datasets to translationally meaningful insights—a multi-marker model for Alzheimer’s disease with improved diagnostic performance beyond pTau217 alone—while maintaining the rigor required for downstream validation.

By lowering the reliance on specialized bioinformatics skills, the agentic workflow helps scientific teams engage more directly with complex biomarker data, test hypotheses more efficiently, and strengthen the evidence needed to prioritize candidate markers with confidence.