AI and the World's Hard Problems · Medicine & drug discovery · Nature (computational biology) · 2026-10-26
Deep learning mitigates batch effects in Raman spectroscopy
Raman spectra from biological samples carry useful molecular information, but their sensitivity also encodes extraneous details such as patient identity, instrument signatures and handling conditions, a so-called batch effect that can cause overfitting and privacy risks. Using a glioma tumor dataset, the authors show that deep learning can predict patient identity from individual spectra. They introduce an adversarial deep learning framework that reduces sample-specific confounding signals while preserving information needed for tumor classification.