{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T08:10:22Z","timestamp":1775031022352,"version":"3.50.1"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"1","funder":[{"name":"Narodowe Centrum Badan i Rozwoju (PL)","award":["STRATEGMED3\/304586\/5\/NCBR\/2017"],"award-info":[{"award-number":["STRATEGMED3\/304586\/5\/NCBR\/2017"]}]},{"name":"Narodowe Centrum Badan i Rozwoju (PL)","award":["POIG.02.03.01-24-099\/13"],"award-info":[{"award-number":["POIG.02.03.01-24-099\/13"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1186\/s12859-017-1693-x","type":"journal-article","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T10:22:11Z","timestamp":1496139731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Learning rule sets from survival data"],"prefix":"10.1186","volume":"18","author":[{"given":"\u0141ukasz","family":"Wr\u00f3bel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Gudy\u015b","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marek","family":"Sikora","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,5,30]]},"reference":[{"key":"1693_CR1","volume-title":"The Statistical Analysis of Failure Time Data","author":"JD Kalbfleisch","year":"2011","unstructured":"Kalbfleisch JD, Prentice RL, Vol. 360. The Statistical Analysis of Failure Time Data. Hoboken: John Wiley & Sons; 2011."},{"issue":"2","key":"1693_CR2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox DR. Regression models and life-tables. J R Stat Soc Series B (Methodological). 1972; 34(2):187\u2013220.","journal-title":"J R Stat Soc Series B (Methodological)"},{"key":"1693_CR3","volume-title":"Advanced Topics in Artificial Intelligence. LNCS","author":"G Holmes","year":"1999","unstructured":"Holmes G, Hall M, Frank E. Generating Rule Sets from Model Trees In: Foo N, editor. Advanced Topics in Artificial Intelligence. LNCS. Berlin Heidelberg: Springer: 1999. p. 1\u201312."},{"key":"1693_CR4","volume-title":"IJCAI-11","author":"F Janssen","year":"2011","unstructured":"Janssen F, F\u00fcrnkranz J. Heuristic Rule-Based Regression via Dynamic Reduction to Classification In: Walsh T, editor. IJCAI-11. Menlo Park: AAAI Press: 2011. p. 1330\u20135."},{"key":"1693_CR5","volume-title":"Artificial Intelligence: Methodology, Systems, and Applications. LNCS","author":"M Sikora","year":"2012","unstructured":"Sikora M, Skowron A, Wr\u00f3bel L. Rule quality measure-based induction of unordered sets of regression rules In: Ramsay A, Agre G, editors. Artificial Intelligence: Methodology, Systems, and Applications. LNCS. Berlin Heidelberg: Springer: 2012. p. 162\u201371."},{"issue":"3","key":"1693_CR6","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1093\/biomet\/69.3.553","volume":"69","author":"DP Harrington","year":"1982","unstructured":"Harrington DP, Fleming TR. A class of rank test procedures for censored survival data. Biometrika. 1982; 69(3):553\u201366.","journal-title":"Biometrika"},{"key":"1693_CR7","volume-title":"IJCAI-73","author":"RS Michalski","year":"1973","unstructured":"Michalski RS. Discovering classification rules using variable-valued logic system VL. In: IJCAI-73. San Francisco: William Kaufmann: 1973. p. 162\u201372."},{"issue":"1","key":"1693_CR8","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1023\/A:1006524209794","volume":"13","author":"J F\u00fcrnkranz","year":"1999","unstructured":"F\u00fcrnkranz J. Separate-and-conquer rule learning. Artif Intell Rev. 1999; 13(1):3\u201354.","journal-title":"Artif Intell Rev"},{"issue":"7","key":"1693_CR9","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1016\/j.camwa.2008.04.030","volume":"56","author":"P Pattaraintakorn","year":"2008","unstructured":"Pattaraintakorn P, Cercone N. A foundation of rough sets theoretical and computational hybrid intelligent system for survival analysis. Comput Math Appl. 2008; 56(7):1699\u2013708.","journal-title":"Comput Math Appl"},{"key":"1693_CR10","volume-title":"Rough Sets and Current Trends in Computing. LNCS","author":"JG Bazan","year":"2002","unstructured":"Bazan JG, Osm\u00f3lski A, Skowron A, \u015alezak D, Szczuka MS, Wr\u00f3blewski J. Rough set approach to the survival analysis In: Alpigini JJ, Peters JF, Skowronek J, Zhong N, editors. Rough Sets and Current Trends in Computing. LNCS. Berlin Heidelberg: Springer: 2002. p. 522\u20139."},{"key":"1693_CR11","first-page":"35","volume":"22","author":"M Sikora","year":"2013","unstructured":"Sikora M, Wr\u00f3bel L, Mielcarek M, Kaw\u0142ak K. Application of rule induction to discover survival factors of patients after bone marrow transplantation. J Med Inform Technol. 2013; 22:35\u201353.","journal-title":"J Med Inform Technol"},{"issue":"16","key":"1693_CR12","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1093\/bioinformatics\/btn265","volume":"24","author":"LP Kronek","year":"2008","unstructured":"Kronek LP, Reddy A. Logical analysis of survival data: prognostic survival models by detecting high-degree interactions in right-censored data. Bioinformatics. 2008; 24(16):248\u201353.","journal-title":"Bioinformatics"},{"key":"1693_CR13","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-28667-4","volume-title":"Three Approaches to Data Analysis. Intelligent Systems Reference Library","author":"I Chikalov","year":"2013","unstructured":"Chikalov I, Lozin V, Lozina I, Moshkov M, Nguyen H, Skowron A, Zielosko B. Logical analysis of data: Theory, methodology and applications. In: Three Approaches to Data Analysis. Intelligent Systems Reference Library. Berlin Heidelberg: Springer: 2013. p. 147\u201392."},{"issue":"1","key":"1693_CR14","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/BF02283750","volume":"16","author":"Y Crama","year":"1988","unstructured":"Crama Y, Hammer PL, Ibaraki T. Cause-effect relationships and partially defined boolean functions. Ann Oper Res. 1988; 16(1):299\u2013325.","journal-title":"Ann Oper Res"},{"issue":"3","key":"1693_CR15","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1081\/BIP-200025657","volume":"14","author":"X Liu","year":"2004","unstructured":"Liu X, Minin V, Huang Y, Seligson DB, Horvath S. Statistical methods for analyzing tissue microarray data. J Biopharm Stat. 2004; 14(3):671\u201385.","journal-title":"J Biopharm Stat"},{"issue":"2","key":"1693_CR16","doi-asserted-by":"crossref","first-page":"411","DOI":"10.2307\/2532300","volume":"48","author":"M LeBlanc","year":"1992","unstructured":"LeBlanc M, Crowley J. Relative risk trees for censored survival data. Biometrics. 1992; 48(2):411\u201325.","journal-title":"Biometrics"},{"issue":"1","key":"1693_CR17","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1093\/biomet\/77.1.147","volume":"77","author":"TM Therneau","year":"1990","unstructured":"Therneau TM, Grambsch PM, Fleming TR. Martingale-based residuals for survival models. Biometrika. 1990; 77(1):147\u201360.","journal-title":"Biometrika"},{"key":"1693_CR18","first-page":"73","volume":"20","author":"\u0141 Wr\u00f3bel","year":"2012","unstructured":"Wr\u00f3bel \u0141. Tree-based induction of decision list from survival data. J Med Inform Technol. 2012; 20:73\u20138.","journal-title":"J Med Inform Technol"},{"key":"1693_CR19","volume-title":"ICML-98","author":"E Frank","year":"1998","unstructured":"Frank E, Witten IH. Generating accurate rule sets without global optimization. In: ICML-98. San Mateo: Morgan Kaufmann: 1998. p. 144\u201351."},{"issue":"2","key":"1693_CR20","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3414\/ME13-01-0046","volume":"53","author":"\u0141 Wr\u00f3bel","year":"2014","unstructured":"Wr\u00f3bel \u0141, Sikora M. Censoring weighted separate-and-conquer rule induction from survival data. Method Inform Med. 2014; 53(2):137\u201348.","journal-title":"Method Inform Med"},{"issue":"3","key":"1693_CR21","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1111\/0824-7935.00154","volume":"17","author":"A An","year":"2001","unstructured":"An A, Cercone N. Rule quality measures for rule induction systems: description and evaluation. Comput Intell. 2001; 17(3):409\u201324.","journal-title":"Comput Intell"},{"issue":"3","key":"1693_CR22","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10994-009-5162-2","volume":"78","author":"F Janssen","year":"2010","unstructured":"Janssen F, F\u00fcrnkranz J. On the quest for optimal rule learning heuristics. Mach Learn. 2010; 78(3):343\u201379.","journal-title":"Mach Learn"},{"issue":"6","key":"1693_CR23","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1080\/03081079.2013.798901","volume":"42","author":"M Sikora","year":"2013","unstructured":"Sikora M, Wr\u00f3bel \u0141. Data-driven adaptive selection of rule quality measures for improving rule induction and filtration algorithms. Int J Gen Syst. 2013; 42(6):594\u2013613.","journal-title":"Int J Gen Syst"},{"issue":"1","key":"1693_CR24","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2307\/2531894","volume":"44","author":"MR Segal","year":"1988","unstructured":"Segal MR. Regression trees for censored data. Biometrics. 1988; 44(1):35\u201347.","journal-title":"Biometrics"},{"issue":"422","key":"1693_CR25","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/01621459.1993.10476296","volume":"88","author":"M LeBlanc","year":"1993","unstructured":"LeBlanc M, Crowley J. Survival trees by goodness of split. J Amer Statist Assoc. 1993; 88(422):457\u201367.","journal-title":"J Amer Statist Assoc"},{"key":"1693_CR26","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1214\/09-SS047","volume":"5","author":"I Bou-Hamad","year":"2011","unstructured":"Bou-Hamad I, Larocque D, Ben-Ameur H. A review of survival trees. Stat Surv. 2011; 5:44\u201371.","journal-title":"Stat Surv"},{"issue":"1","key":"1693_CR27","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1002\/sim.4780140108","volume":"14","author":"D Faraggi","year":"1995","unstructured":"Faraggi D, Simon R. A neural network model for survival data. Stat Med. 1995; 14(1):73\u201382.","journal-title":"Stat Med"},{"issue":"10","key":"1693_CR28","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1002\/(SICI)1097-0258(19980530)17:10<1169::AID-SIM796>3.0.CO;2-D","volume":"17","author":"E Biganzoli","year":"1998","unstructured":"Biganzoli E, Boracchi P, Mariani L, Marubini E. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat Med. 1998; 17(10):1169\u201386.","journal-title":"Stat Med"},{"issue":"5","key":"1693_CR29","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1002\/sim.1655","volume":"23","author":"RM Ripley","year":"2004","unstructured":"Ripley RM, Harris AL, Tarassenko L. Non-linear survival analysis using neural networks. Stat Med. 2004; 23(5):825\u201342.","journal-title":"Stat Med"},{"issue":"3","key":"1693_CR30","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.artmed.2009.08.001","volume":"47","author":"I \u0160tajduhar","year":"2009","unstructured":"\u0160tajduhar I, Dalbelo-Ba\u0161i\u0107 B, Bogunovi\u0107 N. Impact of censoring on learning bayesian networks in survival modelling. Artif Intell Med. 2009; 47(3):199\u2013217.","journal-title":"Artif Intell Med"},{"issue":"4","key":"1693_CR31","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.jbi.2010.03.005","volume":"43","author":"I \u0160tajduhar","year":"2010","unstructured":"\u0160tajduhar I, Dalbelo-Ba\u0161i\u0107 B. Learning bayesian networks from survival data using weighting censored instances. J Biomed Inform. 2010; 43(4):613\u201322.","journal-title":"J Biomed Inform"},{"issue":"1","key":"1693_CR32","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1093\/bioinformatics\/btq617","volume":"27","author":"V Van Belle","year":"2011","unstructured":"Van Belle V, Pelckmans K, Van Huffel S, Suykens JA. Improved performance on high-dimensional survival data by application of Survival-SVM. Bioinformatics. 2011; 27(1):87\u201394.","journal-title":"Bioinformatics"},{"issue":"7","key":"1693_CR33","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1093\/bioinformatics\/btp088","volume":"25","author":"H Binder","year":"2009","unstructured":"Binder H, Allignol A, Schumacher M, Beyersmann J. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics. 2009; 25(7):890\u20136.","journal-title":"Bioinformatics"},{"issue":"3","key":"1693_CR34","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1214\/08-AOAS169","volume":"2","author":"H Ishwaran","year":"2008","unstructured":"Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Statist. 2008; 2(3):841\u201360.","journal-title":"Ann Appl Statist"},{"issue":"1","key":"1693_CR35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1002\/sim.1593","volume":"23","author":"T Hothorn","year":"2004","unstructured":"Hothorn T, Lausen B, Benner A, Radespiel-Tr\u00f6ger M. Bagging survival trees. Stat Med. 2004; 23(1):77\u201391.","journal-title":"Stat Med"},{"key":"1693_CR36","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"1984","unstructured":"Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Belmont: Wadsworth; 1984."},{"issue":"5","key":"1693_CR37","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1055\/s-0038-1634117","volume":"45","author":"M Radespiel-Troger","year":"2006","unstructured":"Radespiel-Troger M, Gefeller O, Rabenstein T, Hothorn T. Association between split selection instability and predictive error in survival trees. Method Inform Med. 2006; 45(5):548\u201356.","journal-title":"Method Inform Med"},{"issue":"1","key":"1693_CR38","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.jmva.2004.02.003","volume":"90","author":"AM Molinaro","year":"2004","unstructured":"Molinaro AM, Dudoit S, Van der Laan MJ. Tree-based multivariate regression and density estimation with right-censored data. J Multivar Anal. 2004; 90(1):154\u201377.","journal-title":"J Multivar Anal"},{"issue":"3","key":"1693_CR39","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1198\/106186006X133933","volume":"15","author":"T Hothorn","year":"2006","unstructured":"Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: A conditional inference framework. J Comp Graph Stat. 2006; 15(3):651\u201374.","journal-title":"J Comp Graph Stat"},{"issue":"2","key":"1693_CR40","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L. Bagging predictors. Mach Learn. 1996; 24(2):123\u201340.","journal-title":"Mach Learn"},{"issue":"1","key":"1693_CR41","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001; 45(1):5\u201332.","journal-title":"Mach Learn"},{"key":"1693_CR42","volume-title":"Compstat","author":"A Benner","year":"2002","unstructured":"Benner A. Application of \u201caggregated classifiers\u201d in survival time studies. In: Compstat. Heidelberg: Springer: 2002. p. 171\u20136."},{"issue":"3","key":"1693_CR43","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1093\/biostatistics\/kxj011","volume":"7","author":"T Hothorn","year":"2006","unstructured":"Hothorn T, Buehlmann P, Dudoit S, Molinaro A, Laan MVD. Survival ensembles. Biostatistics. 2006; 7(3):355\u201373.","journal-title":"Biostatistics"},{"key":"1693_CR44","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.7717\/peerj.1009","volume":"3","author":"L Zhou","year":"2015","unstructured":"Zhou L, Xu Q, Wang H. Rotation survival forest for right censored data. PeerJ. 2015; 3:1009.","journal-title":"PeerJ"},{"issue":"3","key":"1693_CR45","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s11336-014-9413-1","volume":"80","author":"Y Zhou","year":"2015","unstructured":"Zhou Y, McArdle JJ. Rationale and applications of survival tree and survival ensemble methods. Psychometrika. 2015; 80(3):811\u201333.","journal-title":"Psychometrika"},{"key":"1693_CR46","volume-title":"Prognostic Factor Studies","author":"M Schumacher","year":"2006","unstructured":"Schumacher M, Holl\u00e4nder N, Schwarzer G, Sauerbrei W. Prognostic Factor Studies In: Crowley J, Ankerst DP, editors. Prognostic Factor Studies. 2nd edn. Boca Raton: Chapman & Hall\/CRC: 2006. p. 289\u2013333."},{"issue":"10","key":"1693_CR47","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/j.bbmt.2010.04.001","volume":"16","author":"K Kalwak","year":"2010","unstructured":"Kalwak K, Porwolik J, Mielcarek M, Gorczynska E, Owoc-Lempach J, Ussowicz M, Dyla A, Musial J, Pazdzior D, Turkiewicz D, Chybicka A. Higher cd34(+) and cd3(+) cell doses in the graft promote long-term survival, and have no impact on the incidence of severe acute or chronic graft-versus-host disease after in vivo t cell-depleted unrelated donor hematopoietic stem cell transplantation in children. Biol Blood Marrow Transplant. 2010; 16(10):1388\u2013401.","journal-title":"Biol Blood Marrow Transplant"},{"issue":"8","key":"1693_CR48","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/nm733","volume":"8","author":"DG Beer","year":"2002","unstructured":"Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002; 8(8):816\u201324.","journal-title":"Nat Med"},{"issue":"3","key":"1693_CR49","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.cell.2014.09.050","volume":"159","author":"CGAR Network","year":"2014","unstructured":"Network CGAR, et al. Integrated genomic characterization of papillary thyroid carcinoma. Cell. 2014; 159(3):676\u201390.","journal-title":"Cell"},{"issue":"282","key":"1693_CR50","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/01621459.1958.10501452","volume":"53","author":"EL Kaplan","year":"1958","unstructured":"Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958; 53(282):457\u201381.","journal-title":"J Am Stat Assoc"},{"key":"1693_CR51","volume-title":"Statistical Analysis with Missing Data. vol. 539","author":"RJ Little","year":"1987","unstructured":"Little RJ, Rubin DB. Statistical Analysis with Missing Data. vol. 539. New York: Wiley; 1987."},{"issue":"1","key":"1693_CR52","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s10844-010-0121-8","volume":"36","author":"L Wohlrab","year":"2011","unstructured":"Wohlrab L, F\u00fcrnkranz J. A review and comparison of strategies for handling missing values in separate-and-conquer rule learning. J Intell Inf Sys. 2011; 36(1):73\u201398.","journal-title":"J Intell Inf Sys"},{"issue":"10","key":"1693_CR53","doi-asserted-by":"crossref","first-page":"2086","DOI":"10.1200\/JCO.1994.12.10.2086","volume":"12","author":"M Schumacher","year":"1994","unstructured":"Schumacher M, Bastert G, Bojar H, H\u00fcbner K, Olschewski M, Sauerbrei W, Schmoor C, Beyerle C, Neumann R, Rauschecker H, et al. Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. german breast cancer study group. J Clin Oncol. 1994; 12(10):2086.","journal-title":"J Clin Oncol"},{"key":"1693_CR54","unstructured":"cBioPortal of Cancer Genomics. Papillary Thyroid Carcinoma. http:\/\/www.cbioportal.org\/study?id=thca_tcga_pub . Accessed 18 May 2017."},{"key":"1693_CR55","unstructured":"Therneau T, Atkinson B, Ripley B. Rpart: Recursive Partitioning. 2013. R package version 4.1-1, http:\/\/CRAN.R-project.org\/package=rpart . Accessed 18 May 2017."},{"issue":"17-18","key":"1693_CR56","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1002\/(SICI)1097-0258(19990915\/30)18:17\/18<2529::AID-SIM274>3.0.CO;2-5","volume":"18","author":"E Graf","year":"1999","unstructured":"Graf E, Schmoor C, Sauerbrei W, Schumacher M. Assessment and comparison of prognostic classification schemes for survival data. Stat Med. 1999; 18(17-18):2529\u201345.","journal-title":"Stat Med"},{"issue":"5","key":"1693_CR57","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1055\/s-0038-1634384","volume":"42","author":"M Schumacher","year":"2003","unstructured":"Schumacher M, Graf E, Gerds T, et al. How to assess prognostic models for survival data: a case study in oncology. Method Inform Med. 2003; 42(5):564\u201371.","journal-title":"Method Inform Med"},{"key":"1693_CR58","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res. 2006; 7:1\u201330.","journal-title":"J Mach Learn Res"},{"key":"1693_CR59","first-page":"2677","volume":"9","author":"S Garc\u0131a","year":"2008","unstructured":"Garc\u0131a S, Herrera F. An extension on \u201cstatistical comparisons of classifiers over multiple data sets\u201d for all pairwise comparisons. J Mach Learn Res. 2008; 9:2677\u2013694.","journal-title":"J Mach Learn Res"},{"issue":"10","key":"1693_CR60","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","volume":"180","author":"S Garc\u00eda","year":"2010","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Luengo J, Herrera F. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inform Sci. 2010; 180(10):2044\u201364.","journal-title":"Inform Sci"},{"issue":"1","key":"1693_CR61","doi-asserted-by":"crossref","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 Series B (Methodological). 1995; 57(1):289\u2013300.","journal-title":"J R Stat Soc Series B (Methodological)"},{"issue":"41","key":"1693_CR62","doi-asserted-by":"crossref","first-page":"6455","DOI":"10.1038\/sj.onc.1206739","volume":"22","author":"T Fukushima","year":"2003","unstructured":"Fukushima T, Suzuki S, Mashiko M, Ohtake T, Endo Y, Takebayashi Y, Sekikawa K, Hagiwara K, Takenoshita S. BRAF mutations in papillary carcinomas of the thyroid. Oncogene. 2003; 22(41):6455\u20137.","journal-title":"Oncogene"},{"key":"1693_CR63","doi-asserted-by":"crossref","DOI":"10.1002\/9780470258019","volume-title":"Applied survival analysis: regression modeling of time to event data","author":"DW Hosmer","year":"2008","unstructured":"Hosmer DW, Lemeshow S, May S. Applied survival analysis: regression modeling of time to event data. New York: Wiley; 2008."},{"issue":"3","key":"1693_CR64","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1200\/JCO.1994.12.3.601","volume":"12","author":"CL Loprinzi","year":"1994","unstructured":"Loprinzi CL, Laurie JA, Wieand HS, Krook JE, Novotny PJ, Kugler JW, Bartel J, Law M, Bateman M, Klatt NE. Prospective evaluation of prognostic variables from patient-completed questionnaires. north central cancer treatment group. J Clin Oncol. 1994; 12(3):601\u20137.","journal-title":"J Clin Oncol"},{"key":"1693_CR65","doi-asserted-by":"crossref","DOI":"10.1002\/9780470870709","volume-title":"Competing Risks: a Practical Perspective. vol. 58","author":"M Pintilie","year":"2006","unstructured":"Pintilie M. Competing Risks: a Practical Perspective. vol. 58. New York: John Wiley & Sons; 2006."},{"key":"1693_CR66","volume-title":"Case Studies in Biometry. Wiley series in probability and mathematical statistics: Applied probability and statistics","author":"N Lange","year":"1994","unstructured":"Lange N, Ryan L, Billard L, Brillinger D, Conquest L, Greenhouse J. Case Studies in Biometry. Wiley series in probability and mathematical statistics: Applied probability and statistics. New York: Wiley; 1994."},{"key":"1693_CR67","volume-title":"Statistical Models Based on Counting Processes","author":"PK Andersen","year":"2012","unstructured":"Andersen PK, Borgan O, Gill RD, Keiding N. Statistical Models Based on Counting Processes. Berlin Heidelberg: Springer; 2012."},{"issue":"1","key":"1693_CR68","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/S0025-6196(12)60015-9","volume":"68","author":"RA Kyle","year":"1993","unstructured":"Kyle RA. \u201cBenign\u201d monoclonal gammopathy-after 20 to 35 years of follow-up. Mayo Clin Proc. 1993; 68(1):26\u201336.","journal-title":"Mayo Clin Proc"},{"key":"1693_CR69","volume-title":"Counting Processes and Survival Analysis. vol. 169","author":"TR Fleming","year":"2011","unstructured":"Fleming TR, Harrington DP. Counting Processes and Survival Analysis. vol. 169. New York: John Wiley & Sons; 2011."},{"key":"1693_CR70","volume-title":"Survival Analysis: Techniques for Censored and Truncated Data","author":"JP Klein","year":"2005","unstructured":"Klein JP, Moeschberger ML. Survival Analysis: Techniques for Censored and Truncated Data. Berlin Heidelberg: Springer; 2005."},{"issue":"6","key":"1693_CR71","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1016\/0002-9149(67)90498-5","volume":"19","author":"RH Rosenman","year":"1967","unstructured":"Rosenman RH, Friedman M, Jenkins CD, Straus R, Wurm M, Kositchek R. Clinically unrecognized myocardial infarction in the western collaborative group study. Am J Cardiol. 1967; 19(6):776\u201382.","journal-title":"Am J Cardiol"},{"issue":"4","key":"1693_CR72","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1093\/jnci\/dji042","volume":"97","author":"CC Abnet","year":"2005","unstructured":"Abnet CC, Lai B, Qiao YL, Vogt S, Luo XM, Taylor PR, Dong ZW, Mark SD, Dawsey SM. Zinc concentration in esophageal biopsy specimens measured by x-ray fluorescence and esophageal cancer risk. J Natl Cancer Inst. 2005; 97(4):301\u20136.","journal-title":"J Natl Cancer Inst"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-017-1693-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T03:47:11Z","timestamp":1569383231000},"score":1,"resource":{"primary":{"URL":"http:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-017-1693-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,30]]},"references-count":72,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["1693"],"URL":"https:\/\/doi.org\/10.1186\/s12859-017-1693-x","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,5,30]]},"article-number":"285"}}