L. An, C. A. Finney, A. Shvetcov, The Global Neurodegeneration Proteomics Consortium, J. Vogel
Data leakage is a prevalent yet underappreciated flaw in biomarker discovery studies. Through simulation and real-world proteomic data, we demonstrate that typical pipelines are broadly susceptible to this issue, producing inflated performance estimates, poor generalization, and excess false positives. We further introduce two tools to detect data leakage at the code and manuscript level, providing a practical path toward more rigorous and reproducible biomarker reporting.