{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:40:09Z","timestamp":1776436809169,"version":"3.51.2"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-023-00688-4","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T16:02:14Z","timestamp":1689004934000},"page":"792-798","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Reusability report: Evaluating reproducibility and reusability of a fine-tuned model to predict drug response in cancer patient samples"],"prefix":"10.1038","volume":"5","author":[{"given":"Emily","family":"So","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3535-7356","authenticated-orcid":false,"given":"Fengqing","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9620-3413","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7684-0079","authenticated-orcid":false,"given":"Benjamin","family":"Haibe-Kains","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"688_CR1","doi-asserted-by":"publisher","first-page":"e11017","DOI":"10.15252\/msb.202211017","volume":"18","author":"L Trastulla","year":"2022","unstructured":"Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J. & Iorio, F. Computational estimation of quality and clinical relevance of cancer cell lines. Mol. Syst. Biol. 18, e11017 (2022).","journal-title":"Mol. Syst. Biol."},{"key":"688_CR2","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/s43018-020-00169-2","volume":"2","author":"J Ma","year":"2021","unstructured":"Ma, J. et al. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nat. Cancer 2, 233\u2013244 (2021).","journal-title":"Nat. Cancer"},{"key":"688_CR3","doi-asserted-by":"publisher","first-page":"D955","DOI":"10.1093\/nar\/gks1111","volume":"41","author":"W Yang","year":"2013","unstructured":"Yang, W. et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41, D955\u2013D961 (2013).","journal-title":"Nucleic Acids Res."},{"key":"688_CR4","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.cell.2016.08.041","volume":"167","author":"A Bruna","year":"2016","unstructured":"Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260\u2013274 (2016).","journal-title":"Cell"},{"key":"688_CR5","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1186\/s12859-022-04693-z","volume":"23","author":"P Smirnov","year":"2022","unstructured":"Smirnov, P. et al. Evaluation of statistical approaches for association testing in noisy drug screening data. BMC Bioinf. 23, 188 (2022).","journal-title":"BMC Bioinf."},{"key":"688_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-25974-w","volume":"12","author":"A Mammoliti","year":"2021","unstructured":"Mammoliti, A. et al. Orchestrating and sharing large multimodal data for transparent and reproducible research. Nat. Commun. 12, 5797 (2021).","journal-title":"Nat. Commun."},{"key":"688_CR7","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.1158\/2159-8290.CD-15-0235","volume":"5","author":"B Seashore-Ludlow","year":"2015","unstructured":"Seashore-Ludlow, B. et al. Harnessing co\u00ffnnectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov 5, 1210\u20131223 (2015).","journal-title":"Cancer Discov"},{"key":"688_CR8","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nature17987","volume":"533","author":"PM Haverty","year":"2016","unstructured":"Haverty, P. M. et al. Reproducible pharmacogenomic profiling of cancer cell line panels. Nature 533, 333\u2013337 (2016).","journal-title":"Nature"},{"key":"688_CR9","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1016\/j.cell.2016.06.017","volume":"166","author":"F Iorio","year":"2016","unstructured":"Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740\u2013754 (2016).","journal-title":"Cell"},{"key":"688_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-017-01153-8","volume":"8","author":"Z Safikhani","year":"2017","unstructured":"Safikhani, Z. et al. Gene isoforms as expression-based biomarkers predictive of drug response in vitro. Nat. Commun. 8, 1126 (2017).","journal-title":"Nat. Commun."},{"key":"688_CR11","doi-asserted-by":"publisher","first-page":"E1570","DOI":"10.1073\/pnas.1719577115","volume":"115","author":"KL Thu","year":"2018","unstructured":"Thu, K. L. et al. Disruption of the anaphase-promoting complex confers resistance to TTK inhibitors in triple-negative breast cancer. Proc. Natl Acad. Sci. USA 115, E1570\u2013E1577 (2018).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"688_CR12","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1038\/nm.3954","volume":"21","author":"H Gao","year":"2015","unstructured":"Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318\u20131325 (2015).","journal-title":"Nat. Med."},{"key":"688_CR13","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1001\/jama.2011.593","volume":"305","author":"C Hatzis","year":"2011","unstructured":"Hatzis, C. et al. A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. JAMA 305, 1873\u20131881 (2011).","journal-title":"JAMA"},{"key":"688_CR14","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/s10549-013-2763-z","volume":"143","author":"M Itoh","year":"2014","unstructured":"Itoh, M. et al. Estrogen receptor (ER) mRNA expression and molecular subtype distribution in ER-negative\/progesterone receptor-positive breast cancers. Breast Cancer Res. Treat. 143, 403\u2013409 (2014).","journal-title":"Breast Cancer Res. Treat."},{"key":"688_CR15","doi-asserted-by":"publisher","first-page":"12625","DOI":"10.3390\/ijms232012625","volume":"23","author":"O Baldasici","year":"2022","unstructured":"Baldasici, O. et al. Circulating small EVs miRNAs as predictors of pathological response to neo-adjuvant therapy in breast cancer patients. Int. J. Mol. Sci. 23, 12625 (2022).","journal-title":"Int. J. Mol. Sci."},{"key":"688_CR16","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1158\/1078-0432.CCR-12-1359","volume":"19","author":"CE Horak","year":"2013","unstructured":"Horak, C. E. et al. Biomarker analysis of neoadjuvant doxorubicin\/cyclophosphamide followed by ixabepilone or Paclitaxel in early-stage breast cancer. Clin. Cancer Res. 19, 1587\u20131595 (2013).","journal-title":"Clin. Cancer Res."},{"key":"688_CR17","unstructured":"RECIST 1.1 (EORTC); https:\/\/recist.eortc.org\/recist-1-1-2\/"},{"key":"688_CR18","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.12688\/f1000research.9611.1","volume":"5","author":"Z Safikhani","year":"2016","unstructured":"Safikhani, Z. et al. Revisiting inconsistency in large pharmacogenomic studies. F1000Res. 5, 2333 (2016).","journal-title":"F1000Res."},{"key":"688_CR19","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","volume":"483","author":"J Barretina","year":"2012","unstructured":"Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603\u2013607 (2012).","journal-title":"Nature"},{"key":"688_CR20","doi-asserted-by":"crossref","unstructured":"Raff, E. Research reproducibility as a survival analysis. In Proc. AAAI Conference on Artificial Intelligence Vol. 35, 469\u2013478 (AAAI, 2021).","DOI":"10.1609\/aaai.v35i1.16124"},{"key":"688_CR21","doi-asserted-by":"publisher","unstructured":"Clyburne-Sherin, A., Fei, X. & Green, S. A. Computational reproducibility via containers in psychology. Meta-Psychology https:\/\/doi.org\/10.15626\/MP.2018.892 (2019).","DOI":"10.15626\/MP.2018.892"},{"key":"688_CR22","unstructured":"Reusability Report: Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients (Version 2.5) (Code Ocean); https:\/\/codeocean.com\/capsule\/8411716\/tree\/v2"}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00688-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00688-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00688-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T23:04:59Z","timestamp":1689894299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00688-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":22,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["688"],"URL":"https:\/\/doi.org\/10.1038\/s42256-023-00688-4","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,10]]},"assertion":[{"value":"28 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"B.H.K. is a shareholder and paid consultant for Code Ocean Inc. E.S. is a paid consultant for Code Ocean Inc. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}