A Max Planck Institute team introduced ADAPT‑MS, an informatics framework that trains clinical classifiers on discovery proteomics data and adapts models at the individual‑sample level, aiming to bridge discovery and clinical assay development. Detailed in Nature Communications, the method addresses missing‑data challenges endemic to mass spectrometry‑based discovery proteomics. ADAPT‑MS builds an initial classifier from a relaxed feature list and then retrains the model per sample using only the proteins measured in that sample, mitigating data sparsity issues. Authors say the approach can accelerate translation of proteomic biomarkers into clinically actionable tests and reduce dependence on targeted assay redevelopment. By enabling classifiers to be validated on discovery datasets and handling incomplete measurements, ADAPT‑MS could speed movement from thousands‑protein discovery experiments to deployable diagnostics, though prospective clinical validation remains necessary.