Blog
Expert insights on biomarker discovery, AI applications, and the future of personalized medicine
For biomarker researchers
These guides match what precision medicine teams search for: FDA/EMA validation, machine-learning rigor, and discovery workflows.
FDA Biomarker Qualification Program explained: context of use, three submission stages, analytical and clinical validity, utility evidence, CDx vs LDT, EMA parallels, and how literature fits LOI and QP dossiers.
ML in biomarker validation explained: literature-first feature selection, leakage and batch effects, external validation, TRIPOD reporting, model locking, FDA CDS guidance, and cited PMIDs for clinical teams.
Biomarker discovery and validation explained: BEST categories, phased evidence, analytical and clinical validity, utility studies, replication pitfalls, and how to scope cited literature before wet-lab spend.
A practical map of where AI helps in biomarker discovery: literature mining, omics ML, multimodal models, and what still requires wet-lab validation, external cohorts, and prospective trial design.
Target identification and validation explained: druggable genome, genetic evidence, Open Targets and ChEMBL cross-reference, literature triage, wet-lab validation stages, and failure modes with cited PMIDs.
One query. Structured, graded, and cited. Ready for the room.
