{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T05:05:51Z","timestamp":1776402351595,"version":"3.51.2"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006108","name":"U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["CTSA Grant UL1TR002733"],"award-info":[{"award-number":["CTSA Grant UL1TR002733"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["CTSA Grant UL1TR002733"],"award-info":[{"award-number":["CTSA Grant UL1TR002733"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["CTSA Grant UL1TR002733"],"award-info":[{"award-number":["CTSA Grant UL1TR002733"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences"},{"name":"U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-020-00276-w","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T17:07:03Z","timestamp":1609780023000},"page":"68-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data"],"prefix":"10.1038","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2770-0651","authenticated-orcid":false,"given":"Ruoqi","family":"Liu","sequence":"first","affiliation":[]},{"given":"Lai","family":"Wei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4601-0779","authenticated-orcid":false,"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"276_CR1","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1016\/j.drudis.2015.05.001","volume":"20","author":"J Langedijk","year":"2015","unstructured":"Langedijk, J., Mantel-Teeuwisse, A. K., Slijkerman, D. S. & Schutjens, M.-H. D. Drug repositioning and repurposing: terminology and definitions in literature. Drug Discov. Today 20, 1027\u20131034 (2015).","journal-title":"Drug Discov. Today"},{"key":"276_CR2","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/nrd1468","volume":"3","author":"TT Ashburn","year":"2004","unstructured":"Ashburn, T. T. & Thor, K. B. Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3, 673\u2013683 (2004).","journal-title":"Nat. Rev. Drug Discov."},{"key":"276_CR3","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/nrd.2018.168","volume":"18","author":"S Pushpakom","year":"2019","unstructured":"Pushpakom, S. et al. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18, 41\u201358 (2019).","journal-title":"Nat. Rev. Drug Discov."},{"key":"276_CR4","doi-asserted-by":"publisher","first-page":"35996","DOI":"10.1038\/srep35996","volume":"6","author":"H Luo","year":"2016","unstructured":"Luo, H. et al. DPDR-CPI, a server that predicts drug positioning and drug repositioning via chemical-protein interactome. Sci. Rep. 6, 35996 (2016).","journal-title":"Sci. Rep."},{"key":"276_CR5","doi-asserted-by":"publisher","first-page":"6832","DOI":"10.1021\/jm300576q","volume":"55","author":"S Dakshanamurthy","year":"2012","unstructured":"Dakshanamurthy, S. et al. Predicting new indications for approved drugs using a proteochemometric method. J. Med. Chem. 55, 6832\u20136848 (2012).","journal-title":"J. Med. Chem."},{"key":"276_CR6","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1038\/nbt.2151","volume":"30","author":"P Sanseau","year":"2012","unstructured":"Sanseau, P. et al. Use of genome-wide association studies for drug repositioning. Nat. Biotechnol. 30, 317\u2013320 (2012).","journal-title":"Nat. Biotechnol."},{"key":"276_CR7","doi-asserted-by":"publisher","first-page":"14621","DOI":"10.1073\/pnas.1000138107","volume":"107","author":"F Iorio","year":"2010","unstructured":"Iorio, F. et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl Acad. Sci USA 107, 14621\u201314626 (2010).","journal-title":"Proc. Natl Acad. Sci USA"},{"key":"276_CR8","doi-asserted-by":"publisher","first-page":"96ra77","DOI":"10.1126\/scitranslmed.3001318","volume":"3","author":"M Sirota","year":"2011","unstructured":"Sirota, M. et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl. Med. 3, 96ra77 (2011).","journal-title":"Sci. Transl. Med."},{"key":"276_CR9","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1016\/j.drudis.2011.03.002","volume":"16","author":"NS Buchan","year":"2011","unstructured":"Buchan, N. S. et al. The role of translational bioinformatics in drug discovery. Drug Discov. Today 16, 426\u2013434 (2011).","journal-title":"Drug Discov. Today"},{"key":"276_CR10","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1056\/NEJMsb1609216","volume":"375","author":"RE Sherman","year":"2016","unstructured":"Sherman, R. E. et al. Real-world evidence\u2014what is it and what can it tell us. N. Engl. J. Med. 375, 2293\u20132297 (2016).","journal-title":"N. Engl. J. Med."},{"key":"276_CR11","doi-asserted-by":"publisher","first-page":"2691","DOI":"10.1038\/s41467-018-05116-5","volume":"9","author":"F Cheng","year":"2018","unstructured":"Cheng, F. et al. Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat. Commun. 9, 2691 (2018).","journal-title":"Nat. Commun."},{"key":"276_CR12","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1136\/amiajnl-2014-002649","volume":"22","author":"H Xu","year":"2014","unstructured":"Xu, H. et al. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J. Am. Med. Inform. Assoc. 22, 179\u2013191 (2014).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"276_CR13","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1093\/aje\/kwv254","volume":"183","author":"MA Hern\u00e1n","year":"2016","unstructured":"Hern\u00e1n, M. A. & Robins, J. M. Using big data to emulate a target trial when a randomized trial is not available. Am. J. Epidemiol. 183, 758\u2013764 (2016).","journal-title":"Am. J. Epidemiol."},{"key":"276_CR14","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1001\/jama.297.3.314","volume":"297","author":"RB D\u2019Agostino","year":"2007","unstructured":"D\u2019Agostino, R. B. Estimating treatment effects using observational data. JAMA 297, 314\u2013316 (2007).","journal-title":"JAMA"},{"key":"276_CR15","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997).","journal-title":"Neural Comput."},{"key":"276_CR16","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1111\/1468-0262.00442","volume":"71","author":"K Hirano","year":"2003","unstructured":"Hirano, K., Imbens, G. W. & Ridder, G. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71, 1161\u20131189 (2003).","journal-title":"Econometrica"},{"key":"276_CR17","unstructured":"MarketScan Research Databases. IBM https:\/\/www.ibm.com\/products\/marketscan-research-databases (2020)."},{"key":"276_CR18","unstructured":"Commercial Claims and Encounters: Medicare Supplemental https:\/\/theclearcenter.org\/wp-content\/uploads\/2020\/01\/IBM-MarketScan-User-Guide.pdf (Truven Health Analytics, 2016)."},{"key":"276_CR19","unstructured":"Classification of diseases, functioning, and disability. Centers for Disease Control and Prevention https:\/\/www.cdc.gov\/nchs\/icd\/index.htm (2019)."},{"key":"276_CR20","unstructured":"The Observational Health Data Sciences and Informatics (OHDSI). https:\/\/ohdsi.org\/ (2019)."},{"key":"276_CR21","unstructured":"Causes of heart failure. American Heart Association https:\/\/www.heart.org\/en\/health-topics\/heart-failure\/causes-and-risks-for-heart-failure\/causes-of-heart-failure (2017)."},{"key":"276_CR22","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1161\/01.CIR.97.3.282","volume":"97","author":"M Gheorghiade","year":"1998","unstructured":"Gheorghiade, M. & Bonow, R. O. Chronic heart failure in the united states: a manifestation of coronary artery disease. Circulation 97, 282\u2013289 (1998).","journal-title":"Circulation"},{"key":"276_CR23","unstructured":"Conditions that increase risk for stroke. Centers for Disease Control and Prevention https:\/\/www.cdc.gov\/stroke\/conditions.htm (2018)."},{"key":"276_CR24","unstructured":"Coronary artery disease. Heart and Stroke Foundation of Canada https:\/\/www.heartandstroke.ca\/heart\/conditions\/coronary-artery-disease (2019)."},{"key":"276_CR25","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1080\/00273171.2011.568786","volume":"46","author":"PC Austin","year":"2011","unstructured":"Austin, P. C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav. Res. 46, 399\u2013424 (2011).","journal-title":"Multivariate Behav. Res."},{"key":"276_CR26","first-page":"54","volume":"1","author":"B Efron","year":"1986","unstructured":"Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54\u201375 (1986).","journal-title":"Stat. Sci."},{"key":"276_CR27","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289\u2013300 (1995).","journal-title":"J. R. Stat. Soc. B"},{"key":"276_CR28","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1038\/msb.2009.98","volume":"6","author":"M Kuhn","year":"2010","unstructured":"Kuhn, M., Campillos, M., Letunic, L. J. & Bork, P. A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6, 343 (2010).","journal-title":"Mol. Syst. Biol."},{"key":"276_CR29","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart, D. S. et al. Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic Acids Res. 46, D1074\u2013D1082 (2018).","journal-title":"Nucleic Acids Res."},{"key":"276_CR30","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1016\/0735-1097(94)90641-6","volume":"23","author":"ML Fisher","year":"1994","unstructured":"Fisher, M. L. et al. Beneficial effects of metoprolol in heart failure associated with coronary artery disease: a randomized trial. J. Am. Coll. Cardiol. 23, 943\u2013950 (1994).","journal-title":"J. Am. Coll. Cardiol."},{"key":"276_CR31","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.ajo.2012.03.013","volume":"154","author":"TY Wong","year":"2012","unstructured":"Wong, T. Y., Sim\u00f3, R. & Mitchell, P. Fenofibrate \u2013 a potential systemic treatment for diabetic retinopathy?. Am. J. Ophthalmol. 154, 6\u201312 (2012).","journal-title":"Am. J. Ophthalmol."},{"key":"276_CR32","unstructured":"Hydrochlorothiazide. drugs.com https:\/\/www.drugs.com\/monograph\/hydrochlorothiazide.html (2019)."},{"key":"276_CR33","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1001\/jama.290.21.2805","volume":"290","author":"CJ Pepine","year":"2003","unstructured":"Pepine, C. J. et al. A calcium antagonist vs a non\u2013calcium antagonist hypertension treatment strategy for patients with coronary artery disease: the international verapamil-trandolapril study (invest): a randomized controlled trial. JAMA 290, 2805\u20132816 (2003).","journal-title":"JAMA"},{"key":"276_CR34","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.1161\/01.CIR.91.10.2528","volume":"91","author":"JW Jukema","year":"1995","unstructured":"Jukema, J. W. et al. Effects of lipid lowering by pravastatin on progression and regression of coronary artery disease in symptomatic men with normal to moderately elevated serum cholesterol levels: the regression growth evaluation statin study (regress). Circulation 91, 2528\u20132540 (1995).","journal-title":"Circulation"},{"key":"276_CR35","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/S1071-9164(97)90022-1","volume":"3","author":"J Kjekshus","year":"1997","unstructured":"Kjekshus, J., Pedersen, T. R., Olsson, A. G., F\u00e6rgeman, O. & Py\u00f6r\u00e4l\u00e4, K. The effects of simvastatin on the incidence of heart failure in patients with coronary heart disease. J. Card. Fail. 3, 249\u2013254 (1997).","journal-title":"J. Card. Fail."},{"key":"276_CR36","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1080\/10739680701410827","volume":"14","author":"T Higuchi","year":"2007","unstructured":"Higuchi, T., Abletshauser, C., Nekolla, S. G., Schwaiger, M. & Bengel, F. M. Effect of the angiotensin receptor blocker valsartan on coronary microvascular flow reserve in moderately hypertensive patients with stable coronary artery disease. Microcirculation 14, 805\u2013812 (2007).","journal-title":"Microcirculation"},{"key":"276_CR37","unstructured":"Diltiazem. SIDER http:\/\/sideeffects.embl.de\/drugs\/3075\/ (2019)."},{"key":"276_CR38","doi-asserted-by":"publisher","unstructured":"Ozery-Flato, M., Goldschmidt, Y., Shaham, O., Ravid, S. & Yanover, C. Framework for identifying drug repurposing candidates from observational healthcare data. Preprint at medRxiv https:\/\/doi.org\/10.1101\/2020.01.28.20018366 (2020).","DOI":"10.1101\/2020.01.28.20018366"},{"key":"276_CR39","unstructured":"Shimoni, Y. et al. An evaluation toolkit to guide model selection and cohort definition in causal inference. Preprint at https:\/\/arxiv.org\/abs\/1906.00442 (2019)."},{"key":"276_CR40","unstructured":"Zhang, P., Wang, F., Hu, J. & Sorrentino, R. Exploring the relationship between drug side-effects and therapeutic indications. In AMIA Annual Symposium Proceedings 2013 1568\u20131577 (American Medical Informatics Association, 2013)."},{"key":"276_CR41","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1093\/bioinformatics\/btw770","volume":"33","author":"X Liang","year":"2017","unstructured":"Liang, X. et al. LRSSL: predict and interpret drug\u2013disease associations based on data integration using sparse subspace learning. Bioinformatics 33, 1187\u20131196 (2017).","journal-title":"Bioinformatics"},{"key":"276_CR42","doi-asserted-by":"publisher","first-page":"W492","DOI":"10.1093\/nar\/gkr299","volume":"39","author":"H Luo","year":"2011","unstructured":"Luo, H. et al. DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical\u2013protein interactome. Nucleic Acids Res. 39, W492\u2013W498 (2011).","journal-title":"Nucleic Acids Res."},{"key":"276_CR43","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1093\/bib\/bbr013","volume":"12","author":"JT Dudley","year":"2011","unstructured":"Dudley, J. T., Deshpande, T. & Butte, A. J. Exploiting drug\u2013disease relationships for computational drug repositioning. Brief. Bioinform. 12, 303\u2013311 (2011).","journal-title":"Brief. Bioinform."},{"key":"276_CR44","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/s13321-020-00450-7","volume":"12","author":"TN Jarada","year":"2020","unstructured":"Jarada, T. N., Rokne, J. G. & Alhajj, R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J. Cheminf. 12, 46 (2020).","journal-title":"J. Cheminf."},{"key":"276_CR45","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1038\/msb.2011.26","volume":"7","author":"A Gottlieb","year":"2011","unstructured":"Gottlieb, A., Stein, G. Y., Ruppin, E. & Sharan, R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol. 7, 496 (2011).","journal-title":"Mol. Syst. Biol."},{"key":"276_CR46","doi-asserted-by":"publisher","first-page":"7199","DOI":"10.1200\/JCO.2005.01.149","volume":"23","author":"LV Rubinstein","year":"2005","unstructured":"Rubinstein, L. V. et al. Design issues of randomized phase II trials and a proposal for phase ii screening trials. J. Clin. Oncol. 23, 7199\u20137206 (2005).","journal-title":"J. Clin. Oncol."},{"key":"276_CR47","unstructured":"Metformin to reduce heart failure after myocardial infarction (gips-iii). clinicaltrials.gov https:\/\/clinicaltrials.gov\/ct2\/show\/study\/NCT01217307?term=metformin&cond=Coronary+Artery+Disease&phase=12&draw=2&rank=2 (2018)."},{"key":"276_CR48","unstructured":"Escitalopram oxalate. drugs.com https:\/\/www.drugs.com\/monograph\/escitalopram-oxalate.html (2020)."},{"key":"276_CR49","unstructured":"Responses of myocardial ischemia to escitalopram treatment (remit). clinicaltrials.gov https:\/\/clinicaltrials.gov\/ct2\/show\/NCT00574847?term=escitalopram&cond=Coronary+Artery+Disease&draw=2&rank=1 (2015)."},{"key":"276_CR50","unstructured":"Effect of atorvastatin on fractional flow reserve in coronary artery disease (forte). clinicaltrials.gov https:\/\/clinicaltrials.gov\/ct2\/show\/NCT01946815?term=atorvastatin&cond=Coronary+Artery+Disease&phase=12&draw=2&rank=1 (2018)."},{"key":"276_CR51","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1016\/S0140-6736(02)08089-3","volume":"359","author":"B Dahl\u00f6f","year":"2002","unstructured":"Dahl\u00f6f, B. et al. Cardiovascular morbidity and mortality in the losartan intervention for endpoint reduction in hypertension study (life): a randomised trial against atenolol. Lancet 359, 995\u20131003 (2002).","journal-title":"Lancet"},{"key":"276_CR52","doi-asserted-by":"crossref","unstructured":"D\u2019Agostino, R. B. Jr Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med. 17, 2265\u20132281 (1998).","DOI":"10.1002\/(SICI)1097-0258(19981015)17:19<2265::AID-SIM918>3.0.CO;2-B"}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-00276-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-00276-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-00276-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T15:02:41Z","timestamp":1716476561000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-00276-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["276"],"URL":"https:\/\/doi.org\/10.1038\/s42256-020-00276-w","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]},"assertion":[{"value":"27 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}