{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:38Z","timestamp":1740185138698,"version":"3.37.3"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004326","name":"Bayer AG","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004326","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient datasets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art pre-processing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>FORESEE is licensed under GNU General Public License v3.0 and available at https:\/\/github.com\/JRC-COMBINE\/FORESEE and https:\/\/doi.org\/10.17605\/OSF.IO\/RF6QK, and provides vignettes for documentation and application both online and in the Supplementary Files 2 and 3.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz145","type":"journal-article","created":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T12:13:48Z","timestamp":1551096828000},"page":"3846-3848","source":"Crossref","is-referenced-by-count":6,"title":["FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4431-6360","authenticated-orcid":false,"given":"Lisa-Katrin","family":"Turnhoff","sequence":"first","affiliation":[{"name":"Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University , Aachen, Germany"},{"name":"Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University , Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0027-4813","authenticated-orcid":false,"given":"Ali","family":"Hadizadeh Esfahani","sequence":"additional","affiliation":[{"name":"Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University , Aachen, Germany"},{"name":"Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University , Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4688-9311","authenticated-orcid":false,"given":"Maryam","family":"Montazeri","sequence":"additional","affiliation":[{"name":"Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University , Aachen, Germany"},{"name":"Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University , Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9689-6785","authenticated-orcid":false,"given":"Nina","family":"Kusch","sequence":"additional","affiliation":[{"name":"Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University , Aachen, Germany"},{"name":"Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University , Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3783-6605","authenticated-orcid":false,"given":"Andreas","family":"Schuppert","sequence":"additional","affiliation":[{"name":"Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University , Aachen, Germany"},{"name":"Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University , Aachen, Germany"}]}],"member":"286","published-online":{"date-parts":[[2019,3,1]]},"reference":[{"key":"2023013108185888400_btz145-B1","doi-asserted-by":"crossref","first-page":"i413","DOI":"10.1093\/bioinformatics\/btw449","article-title":"TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types","volume":"32","author":"Aben","year":"2016","journal-title":"Bioinformatics"},{"key":"2023013108185888400_btz145-B2","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023013108185888400_btz145-B3","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/j.cell.2013.08.003","article-title":"An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules","volume":"154","author":"Basu","year":"2013","journal-title":"Cell"},{"key":"2023013108185888400_btz145-B4","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1158\/1078-0432.CCR-12-1558","article-title":"An epithelial-mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance","volume":"19","author":"Byers","year":"2013","journal-title":"Clin. 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