{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T22:23:21Z","timestamp":1773872601334,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T00:00:00Z","timestamp":1551225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T00:00:00Z","timestamp":1551225600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81773541"],"award-info":[{"award-number":["81773541"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81573253"],"award-info":[{"award-number":["81573253"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R03-DE024198"],"award-info":[{"award-number":["R03-DE024198"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R03-DE025646"],"award-info":[{"award-number":["R03-DE025646"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1186\/s12859-019-2656-1","type":"journal-article","created":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T11:06:06Z","timestamp":1551265566000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information"],"prefix":"10.1186","volume":"20","author":[{"given":"Zaixiang","family":"Tang","sequence":"first","affiliation":[]},{"given":"Shufeng","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Xinyan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zixuan","family":"Yi","sequence":"additional","affiliation":[]},{"given":"Boyi","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Jake Y.","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yueping","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Nengjun","family":"Yi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,2,27]]},"reference":[{"key":"2656_CR1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Statistical Soc Series B. 1996;58:267\u201388.","journal-title":"J Royal Statistical Soc Series B"},{"issue":"4","key":"2656_CR2","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3","volume":"16","author":"R Tibshirani","year":"1997","unstructured":"Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385\u201395.","journal-title":"Stat Med"},{"key":"2656_CR3","volume-title":"Penalized linear unbiased selection","author":"C Zhang","year":"2007","unstructured":"Zhang C. Penalized linear unbiased selection. Rutgers University: Department of Statistics and Bioinformatics; 2007. Technical Report #2007\u20132003"},{"key":"2656_CR4","first-page":"894","volume-title":"Nearly unbiased variable selection under minimax concave penalty","author":"C-H Zhang","year":"2010","unstructured":"Zhang C-H. Nearly unbiased variable selection under minimax concave penalty; 2010. p. 894\u2013942."},{"issue":"456","key":"2656_CR5","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1198\/016214501753382273","volume":"96","author":"J Fan","year":"2001","unstructured":"Fan J, Li R. Variable selection via nonconcave penalized likelihood and its Oracle properties. J Am Stat Assoc. 2001;96(456):1348\u201360.","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"2656_CR6","doi-asserted-by":"publisher","first-page":"e1002975","DOI":"10.1371\/journal.pcbi.1002975","volume":"9","author":"W Zhang","year":"2013","unstructured":"Zhang W, Ota T, Shridhar V, Chien J, Wu B, Kuang R. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment. PLoS Comput Biol. 2013;9(3):e1002975.","journal-title":"PLoS Comput Biol"},{"issue":"7","key":"2656_CR7","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1038\/nbt.2940","volume":"32","author":"Y Yuan","year":"2014","unstructured":"Yuan Y, Van Allen EM, Omberg L, Wagle N, Amin-Mansour A, Sokolov A, Byers LA, Xu Y, Hess KR, Diao L, et al. Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat Biotechnol. 2014;32(7):644\u201352.","journal-title":"Nat Biotechnol"},{"issue":"1","key":"2656_CR8","doi-asserted-by":"publisher","first-page":"e54089","DOI":"10.1371\/journal.pone.0054089","volume":"8","author":"I Sohn","year":"2013","unstructured":"Sohn I, Sung CO. Predictive modeling using a somatic mutational profile in ovarian high grade serous carcinoma. PLoS One. 2013;8(1):e54089.","journal-title":"PLoS One"},{"issue":"1","key":"2656_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-8-35","volume":"8","author":"F Rapaport","year":"2007","unstructured":"Rapaport F, Zinovyev A, Dutreix M, Barillot E, Vert J-P. Classification of microarray data using gene networks. BMC Bioinformatics. 2007;8(1):1\u201315.","journal-title":"BMC Bioinformatics"},{"key":"2656_CR10","doi-asserted-by":"publisher","DOI":"10.1201\/b12677","volume-title":"Computational systems biology of Cancer Chapman & Hall\/CRC Mathematical & Computational Biology","author":"E Barillot","year":"2012","unstructured":"Barillot E, Calzone L, Hupe P, Vert JP, Zinovyev A. Computational systems biology of Cancer Chapman & Hall\/CRC Mathematical & Computational Biology; 2012."},{"issue":"2","key":"2656_CR11","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1093\/bib\/bbu003","volume":"16","author":"Q Zhao","year":"2015","unstructured":"Zhao Q, Shi X, Xie Y, Huang J, Shia B, Ma S. Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA. Brief Bioinform. 2015;16(2):291\u2013303.","journal-title":"Brief Bioinform"},{"issue":"1","key":"2656_CR12","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B. 2006;68(1):49\u201367.","journal-title":"J R Stat Soc Ser B"},{"key":"2656_CR13","volume-title":"A note on the group lasso and a sparse group lasso","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R. A note on the group lasso and a sparse group lasso. Stanford University: Technical report, Department of Statistics; 2010."},{"issue":"2","key":"2656_CR14","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1080\/10618600.2012.681250","volume":"22","author":"N Simon","year":"2013","unstructured":"Simon N, Friedman J, Hastie T, Tibshirani R. A sparse-group lasso. J Comput Graph Stat. 2013;22(2):231\u201345.","journal-title":"J Comput Graph Stat"},{"issue":"2","key":"2656_CR15","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1093\/biomet\/asp020","volume":"96","author":"J Huang","year":"2009","unstructured":"Huang J, Ma S, Xie H, Zhang C-H. A group bridge approach for variable selection. Biometrika. 2009;96(2):339\u201355.","journal-title":"Biometrika"},{"issue":"3","key":"2656_CR16","doi-asserted-by":"publisher","first-page":"369","DOI":"10.4310\/SII.2009.v2.n3.a10","volume":"2","author":"P Breheny","year":"2009","unstructured":"Breheny P, Huang J. Penalized methods for bi-level variable selection. Statistics and its interface. 2009;2(3):369\u201380.","journal-title":"Statistics and its interface"},{"issue":"6A","key":"2656_CR17","doi-asserted-by":"publisher","first-page":"3468","DOI":"10.1214\/07-AOS584","volume":"37","author":"P Zhao","year":"2009","unstructured":"Zhao P, Rocha G, Yu B. The composite absolute penalties family for grouped and hierarchical variable selection. Ann Stat. 2009;37(6A):3468\u201397.","journal-title":"Ann Stat"},{"issue":"3","key":"2656_CR18","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1111\/biom.12300","volume":"71","author":"P Breheny","year":"2015","unstructured":"Breheny P. The group exponential lasso for bi-level variable selection. Biometrics. 2015;71(3):731\u201340.","journal-title":"Biometrics"},{"issue":"1","key":"2656_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/wics.1284","volume":"6","author":"Y Chen","year":"2014","unstructured":"Chen Y, Du P, Wang Y. Variable selection in linear models. Wiley Interdisciplinary Reviews: Computational Statistics. 2014;6(1):1\u20139.","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"issue":"5","key":"2656_CR20","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1007\/s10463-016-0571-z","volume":"69","author":"S Kwon","year":"2017","unstructured":"Kwon S, Ahn J, Jang W, Lee S, Kim Y. A doubly sparse approach for group variable selection. Ann Inst Stat Math. 2017;69(5):997\u20131025.","journal-title":"Ann Inst Stat Math"},{"key":"2656_CR21","doi-asserted-by":"crossref","unstructured":"Huang J, Breheny P, Ma S. A selective review of group selection in high-dimensional models. Stat Sci. 2012;27(4).","DOI":"10.1214\/12-STS392"},{"issue":"Suppl 5","key":"2656_CR22","doi-asserted-by":"publisher","first-page":"S7","DOI":"10.1186\/1753-6561-8-S5-S7","volume":"8","author":"JO Ogutu","year":"2014","unstructured":"Ogutu JO, Piepho HP. Regularized group regression methods for genomic prediction: bridge, MCP, SCAD, group bridge, group lasso, sparse group lasso, group MCP and group SCAD. BMC Proc. 2014;8(Suppl 5):S7.","journal-title":"BMC Proc"},{"key":"2656_CR23","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/978-3-319-27099-9_11","volume-title":"Statistical analysis for high-dimensional data: the Abel symposium","author":"V Ro\u010dkov\u00e1","year":"2016","unstructured":"Ro\u010dkov\u00e1 V, George EI. Bayesian penalty mixing: the case of a non-separable penalty. In: Frigessi A, B\u00fchlmann P, Glad IK, Langaas M, Richardson S, Vannucci M, editors. Statistical analysis for high-dimensional data: the Abel symposium, vol. 2014. Cham: Springer International Publishing; 2016. p. 233\u201354."},{"key":"2656_CR24","doi-asserted-by":"publisher","unstructured":"Ro\u010dkov\u00e1 V, George EI: The spike-and-slab lasso. J Am Stat Assoc 2016:Online, DOI: https:\/\/doi.org\/10.1080\/01621459.01622016.01260469 .","DOI":"10.1080\/01621459.01622016.01260469"},{"issue":"1","key":"2656_CR25","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1534\/genetics.116.192195","volume":"205","author":"Z Tang","year":"2017","unstructured":"Tang Z, Shen Y, Zhang X, Yi N. The spike-and-slab lasso generalized linear models for prediction and associated genes detection. Genetics. 2017;205(1):77\u201388.","journal-title":"Genetics"},{"issue":"18","key":"2656_CR26","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1093\/bioinformatics\/btx300","volume":"33","author":"Z Tang","year":"2017","unstructured":"Tang Z, Shen Y, Zhang X, Yi N. The spike-and-slab lasso Cox model for survival prediction and associated genes detection. Bioinformatics. 2017;33(18):2799\u2013807.","journal-title":"Bioinformatics"},{"issue":"6","key":"2656_CR27","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1093\/bioinformatics\/btx684","volume":"34","author":"Z Tang","year":"2018","unstructured":"Tang Z, Shen Y, Li Y, Zhang X, Wen J, Qian C, Zhuang W, Shi X, Yi N. Group spike-and-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information. Bioinformatics. 2018;34(6):901\u201310.","journal-title":"Bioinformatics"},{"issue":"1","key":"2656_CR28","doi-asserted-by":"publisher","first-page":"Article 7","DOI":"10.2202\/1544-6115.1755","volume":"11","author":"M Silver","year":"2012","unstructured":"Silver M, Montana G. Alzheimer\u2019s disease neuroimaging I: fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps. Stat Appl Genet Mol Biol. 2012;11(1):Article 7.","journal-title":"Stat Appl Genet Mol Biol"},{"issue":"11","key":"2656_CR29","doi-asserted-by":"publisher","first-page":"e1003939","DOI":"10.1371\/journal.pgen.1003939","volume":"9","author":"M Silver","year":"2013","unstructured":"Silver M, Chen P, Li R, Cheng CY, Wong TY, Tai ES, Teo YY, Montana G. Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts. PLoS Genet. 2013;9(11):e1003939.","journal-title":"PLoS Genet"},{"key":"2656_CR30","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1145\/1553374.1553431","volume-title":"Proceedings of the 26th Annual International Conference on Machine Learning","author":"L Jacob","year":"2009","unstructured":"Jacob L, Obozinski G, Vert J-P. Group lasso with overlap and graph lasso. In: Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, Quebec, Canada: 1553431: ACM; 2009. p. 433\u201340."},{"key":"2656_CR31","doi-asserted-by":"publisher","DOI":"10.1201\/b18401","volume-title":"Statistical learning with sparsity - the lasso and generalization","author":"T Hastie","year":"2015","unstructured":"Hastie T, Tibshirani R, Wainwright M. Statistical learning with sparsity - the lasso and generalization. New York: CRC Press; 2015."},{"key":"2656_CR32","doi-asserted-by":"crossref","unstructured":"Klein J, Moeschberger M. Survival Analysis. New York: Springer-Verlag; 2003.","DOI":"10.1007\/b97377"},{"key":"2656_CR33","doi-asserted-by":"crossref","unstructured":"Ibrahim J, Chen M-H, Debajyoti S. Bayesian survival analysis. New York: Springer-Verlag; 2001.","DOI":"10.1007\/978-1-4757-3447-8"},{"key":"2656_CR34","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. 1972;34:187\u2013220.","journal-title":"J R Stat Soc"},{"key":"2656_CR35","first-page":"216","volume":"34","author":"NE Breslow","year":"1972","unstructured":"Breslow NE. Contribution to the discussion of the paper by D. R. Cox. J Royal Stat Soc B. 1972;34:216\u20137.","journal-title":"J Royal Stat Soc B"},{"key":"2656_CR36","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1080\/01621459.1977.10480613","volume":"72","author":"B Efron","year":"1977","unstructured":"Efron B. The efficiency of Cox's likelihood function for censored data. J Am Stat Assoc. 1977;72:557\u201365.","journal-title":"J Am Stat Assoc"},{"key":"2656_CR37","doi-asserted-by":"crossref","unstructured":"van Houwelinggen HG, Putter H. Dynamic prediction in clinical survival analysis. Boca Raton: CRC Press; 2012.","DOI":"10.1201\/b11311"},{"key":"2656_CR38","volume-title":"Data analysis using regression and multilevel\/hierarchical models","author":"A Gelman","year":"2007","unstructured":"Gelman A, Hill J. Data analysis using regression and multilevel\/hierarchical models. New York: Cambridge University Press; 2007."},{"key":"2656_CR39","volume-title":"Bayesian data analysis","author":"A Gelman","year":"2014","unstructured":"Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian data analysis. Third ed. New York: Chapman & Hall\/CRC Press; 2014.","edition":"Third"},{"key":"2656_CR40","doi-asserted-by":"publisher","first-page":"89","DOI":"10.2307\/2529620","volume":"30","author":"N Breslow","year":"1974","unstructured":"Breslow N. Covariance analysis of censored survival data. Biometrics. 1974;30:89\u201399.","journal-title":"Biometrics"},{"issue":"5","key":"2656_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v039.i05","volume":"39","author":"N Simon","year":"2011","unstructured":"Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox's proportional hazards model via coordinate descent. J Stat Softw. 2011;39(5):1\u201313.","journal-title":"J Stat Softw"},{"issue":"18","key":"2656_CR42","doi-asserted-by":"publisher","first-page":"3201","DOI":"10.1002\/sim.2353","volume":"25","author":"HC van Houwelingen","year":"2006","unstructured":"van Houwelingen HC, Bruinsma T, Hart AA, Van\u2019t Veer LJ, Wessels LF. Cross-validated Cox regression on microarray gene expression data. Stat Med. 2006;25(18):3201\u201316.","journal-title":"Stat Med"},{"key":"2656_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2202\/1544-6115.1000","volume":"1","author":"RJ Tibshirani","year":"2002","unstructured":"Tibshirani RJ, Efron B. Pre-validation and inference in microarrays. Stat Appl Genet Mol Biol. 2002;1:1\u201318.","journal-title":"Stat Appl Genet Mol Biol"},{"key":"2656_CR44","doi-asserted-by":"publisher","unstructured":"Yi N, Tang Z, Zhang X, Guo B. BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology. Bioinformatics. 2018. https:\/\/doi.org\/10.1093\/bioinformatics\/bty803 .","DOI":"10.1093\/bioinformatics\/bty803"},{"key":"2656_CR45","doi-asserted-by":"publisher","first-page":"179","DOI":"10.4137\/CIN.S40043","volume":"15","author":"Y Zeng","year":"2016","unstructured":"Zeng Y, Breheny P. Overlapping group logistic regression with applications to genetic pathway selection. Cancer Informat. 2016;15:179\u201387.","journal-title":"Cancer Informat"},{"issue":"5","key":"2656_CR46","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","volume":"16","author":"G Yu","year":"2012","unstructured":"Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics : a journal of integrative biology. 2012;16(5):284\u20137.","journal-title":"Omics : a journal of integrative biology"},{"issue":"5","key":"2656_CR47","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1056\/NEJMoa1502449","volume":"373","author":"SK Gara","year":"2015","unstructured":"Gara SK, Jia L, Merino MJ, Agarwal SK, Zhang L, Cam M, Patel D, Kebebew E. Germline HABP2 mutation causing familial nonmedullary thyroid Cancer. N Engl J Med. 2015;373(5):448\u201355.","journal-title":"N Engl J Med"},{"key":"2656_CR48","doi-asserted-by":"crossref","unstructured":"Zhu M, Qiu S, Zhang X, Wang Y, Souraka TDM, Wen X, Liang C, Tu J. The associations between CYP24A1 polymorphisms and cancer susceptibility: a meta-analysis and trial sequential analysis. Pathology - Research and Practice. 2018;214(1):53-63.","DOI":"10.1016\/j.prp.2017.11.014"},{"issue":"44","key":"2656_CR49","doi-asserted-by":"publisher","first-page":"76189","DOI":"10.18632\/oncotarget.19198","volume":"8","author":"HS Tan","year":"2017","unstructured":"Tan HS, Jiang WH, He Y, Wang DS, Wu ZJ, Wu DS, Gao L, Bao Y, Shi JZ, Liu B, et al. KRT8 upregulation promotes tumor metastasis and is predictive of a poor prognosis in clear cell renal cell carcinoma. Oncotarget. 2017;8(44):76189\u2013203.","journal-title":"Oncotarget"},{"issue":"2","key":"2656_CR50","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1111\/cas.13120","volume":"108","author":"J Fang","year":"2017","unstructured":"Fang J, Wang H, Liu Y, Ding F, Ni Y, Shao S. High KRT8 expression promotes tumor progression and metastasis of gastric cancer. Cancer Sci. 2017;108(2):178\u201386.","journal-title":"Cancer Sci"},{"issue":"31","key":"2656_CR51","doi-asserted-by":"publisher","first-page":"31944","DOI":"10.18632\/oncotarget.5128","volume":"6","author":"J Chu","year":"2015","unstructured":"Chu J, Zhu Y, Liu Y, Sun L, Lv X, Wu Y, Hu P, Su F, Gong C, Song E, et al. E2F7 overexpression leads to tamoxifen resistance in breast cancer cells by competing with E2F1 at miR-15a\/16 promoter. Oncotarget. 2015;6(31):31944\u201357.","journal-title":"Oncotarget"},{"key":"2656_CR52","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.jocn.2016.04.019","volume":"33","author":"W Yin","year":"2016","unstructured":"Yin W, Wang B, Ding M, Huo Y, Hu H, Cai R, Zhou T, Gao Z, Wang Z, Chen D. Elevated E2F7 expression predicts poor prognosis in human patients with gliomas. J Clin Neurosci. 2016;33:187\u201393.","journal-title":"J Clin Neurosci"},{"issue":"8","key":"2656_CR53","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1158\/1535-7163.MCT-15-0076","volume":"14","author":"M Hazar-Rethinam","year":"2015","unstructured":"Hazar-Rethinam M, de Long LM, Gannon OM, Boros S, Vargas AC, Dzienis M, Mukhopadhyay P, Saenz-Ponce N, Dantzic DDE, Simpson F, et al. RacGAP1 is a novel downstream effector of E2F7-dependent resistance to doxorubicin and is prognostic for overall survival in squamous cell carcinoma. Mol Cancer Ther. 2015;14(8):1939\u201350.","journal-title":"Mol Cancer Ther"},{"issue":"1","key":"2656_CR54","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1111\/j.1467-9868.2007.00627.x","volume":"70","author":"L Meier","year":"2008","unstructured":"Meier L, van de Geer S, B\u00fchlmann P. The group lasso for logistic regression. J Royal Stat Soc Series B. 2008;70(1):53\u201371.","journal-title":"J Royal Stat Soc Series B"},{"key":"2656_CR55","volume-title":"Group variable selection via a hierarchical lasso and its Oracle property","author":"N Zhou","year":"2011","unstructured":"Zhou N, Zhu J. Group variable selection via a hierarchical lasso and its Oracle property; 2011."},{"issue":"504","key":"2656_CR56","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1080\/01621459.2013.869223","volume":"109","author":"V Ro\u010dkov\u00e1","year":"2014","unstructured":"Ro\u010dkov\u00e1 V, George EI. EMVS: the EM approach to Bayesian variable selection. J Am Stat Assoc. 2014;109(504):828\u201346.","journal-title":"J Am Stat Assoc"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-2656-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-2656-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-2656-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T04:55:42Z","timestamp":1721019342000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-2656-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,27]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["2656"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-2656-1","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,27]]},"assertion":[{"value":"21 May 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"94"}}