BRIDGE-KO: auditable transfer learning with bridged knockoffs improves cross-cohort microbiome prediction of immunotherapy response.

Transfer learning promises to reuse AI models across biomedical studies, but it often fails when differences between datasets introduce cohort-specific artifacts and 'negative transfer.' We present BRIDGE-KO, an interpretable deep-learning framework that turns transfer learning into a testable design process by asking: where to transfer from (source-target coverage and proximity), what to transfer (which representation level is most portable), and how to harmonize data to reduce technical confounding. BRIDGE-KO creates source-preserving knockoffs-synthetic 'shadow' features that capture the source dependency structure-and bridges them to a fixed target cohort, providing built-in baselines that flag non-portable signal. Applied to predicting immune checkpoint inhibitor response, transferring gut microbiome profiles from MicroBioMap (n = 168,464) to melanoma immune checkpoint inhibitor cohorts (n = 154), naive baselines performed near chance (AUC ≈ 0.52), whereas batch correction plus proximity-aware transfer improved performance (AUC = 0.67, 10 splits). BRIDGE-KO also enables an interpretable responder-specific audit of microbiome remodeling via correlation-shift spread.