{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T15:49:05Z","timestamp":1791906545691,"version":"4.3.4"},"reference-count":67,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":42,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM108618"],"award-info":[{"award-number":["R01GM108618"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Transfer learning promises to reuse AI models across biomedical studies, but it often fails when differences between datasets introduce cohort-specific artifacts and \u2018negative transfer.\u2019 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\u2014synthetic \u2018shadow\u2019 features that capture the source dependency structure\u2014and 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\u2009=\u2009168,464) to melanoma immune checkpoint inhibitor cohorts (n\u2009=\u2009154), naive baselines performed near chance (AUC\u2009\u2248\u20090.52), whereas batch correction plus proximity-aware transfer improved performance (AUC\u2009=\u20090.67, 10 splits). BRIDGE-KO also enables an interpretable responder-specific audit of microbiome remodeling via correlation-shift spread.<\/jats:p>","DOI":"10.1093\/bib\/bbag545","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:28:30Z","timestamp":1789756110000},"source":"Crossref","is-referenced-by-count":0,"title":["BRIDGE-KO: auditable transfer learning with bridged knockoffs improves cross-cohort microbiome prediction of immunotherapy response"],"prefix":"10.1093","volume":"27","author":[{"given":"Jie","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Human Genetics, University of Pittsburgh , 4200 Fifth Avenue, Pittsburgh, PA 15260 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenjiang","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Adult Neurosurgery, School of Medicine, Stanford University , 450 Jane Stanford Way, Stanford, CA 94305 ,","place":["United 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