{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:24:07Z","timestamp":1784766247719,"version":"3.55.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Federal Ministry of Education and Research","doi-asserted-by":"publisher","award":["FKZ 01ZX1903A"],"award-info":[{"award-number":["FKZ 01ZX1903A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"name":"AntifibrotiX","award":["FKZ 03VP06230"],"award-info":[{"award-number":["FKZ 03VP06230"]}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["633589"],"award-info":[{"award-number":["633589"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Accurate transfer learning of clinical outcomes from one cellular context to another, between cell types, developmental stages, omics modalities or species, is considered tremendously useful. When transferring a prediction task from a source domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring states or processes common to both the source and the target that can be learned by the predictor reflected by shared denominators. These may form a compendium of knowledge that is learned in the source to enable predictions in the target, usually with few, if any, labeled target training samples to learn from. Transductive transfer learning refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive transfer learning considers cases where the target predictor is performing a different yet related task as compared with the source predictor. Often, there is also a need to first map the variables in the input\/feature spaces and\/or the variables in the output\/outcome spaces. We here discuss and juxtapose various recently published transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in vivo) outcomes based on preclinical (mostly animal-based) molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.<\/jats:p>","DOI":"10.1093\/bib\/bbac133","type":"journal-article","created":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T11:06:50Z","timestamp":1648465610000},"source":"Crossref","is-referenced-by-count":18,"title":["Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives"],"prefix":"10.1093","volume":"23","author":[{"given":"Axel","family":"Kowald","sequence":"first","affiliation":[{"name":"Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Israel","family":"Barrantes","sequence":"additional","affiliation":[{"name":"Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"M\u00f6ller","sequence":"additional","affiliation":[{"name":"Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Palmer","sequence":"additional","affiliation":[{"name":"Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugo","family":"Murua Escobar","sequence":"additional","affiliation":[{"name":"Department of Medicine, Clinic III, Hematology, Oncology, Palliative Medicine, Rostock University Medical Center, Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Schwerk","sequence":"additional","affiliation":[{"name":"CENTOGENE GmbH, Am Strande 7, Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4994-9829","authenticated-orcid":false,"given":"Georg","family":"Fuellen","sequence":"additional","affiliation":[{"name":"Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany"},{"name":"Centre for Transdisciplinary Neurosciences Rostock, Research Focus Oncology and Ageing of Individuals and Society, Interdisciplinary Faculty, Rostock, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,4,23]]},"reference":[{"key":"2022051813450418600_ref1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/s41573-021-00301-6","article-title":"Harnessing the predictive power of preclinical models for oncology drug development","volume":"21","author":"Honkala","year":"2022","journal-title":"Nat Rev Drug Discov"},{"key":"2022051813450418600_ref2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.cotox.2020.02.003","article-title":"Translating in vitro to in vivo and animal to human","volume":"23-24","author":"Steger-Hartmann","year":"2020","journal-title":"Curr Opin Toxicol"},{"key":"2022051813450418600_ref3","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.ebiom.2019.08.027","article-title":"Looking beyond the hype: applied AI and machine learning in translational medicine","volume":"47","author":"Toh","year":"2019","journal-title":"EBioMedicine"},{"key":"2022051813450418600_ref4","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1111\/imcb.12404","article-title":"The future is now? 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