A 2025 perspective on the role of machine learning for biomarker discovery in clinical proteomics

We caution against the uncritical application of complex models, such as deep learning architectures, that often exacerbate these problems, offering limited interpretability and negligible performance gains in typical clinical proteomics datasets. Instead, we advocate for the realistic and responsible use of machine learning, grounded in rigorous study design, appropriate validation strategies, and transparent, reproducible modeling practices. Emphasizing simplicity, interpretability, and domain awareness over hype-driven complexity is essential if machine learning is to fulfill its translational potential in the clinic.