{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:40:35Z","timestamp":1758271235291,"version":"3.37.3"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T00:00:00Z","timestamp":1603152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T00:00:00Z","timestamp":1603152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000092","name":"U.S. National Library of Medicine","doi-asserted-by":"publisher","award":["K01LM012426"],"award-info":[{"award-number":["K01LM012426"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U01CA196386","P30CA023108","CA023108","U19CA203654"],"award-info":[{"award-number":["U01CA196386","P30CA023108","CA023108","U19CA203654"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["P20GM130454"],"award-info":[{"award-number":["P20GM130454"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004917","name":"Cancer Prevention and Research Institute of Texas","doi-asserted-by":"publisher","award":["RR170048"],"award-info":[{"award-number":["RR170048"]}],"id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Genomic profiling of solid human tumors by projects such as The Cancer Genome Atlas (TCGA) has provided important information regarding the somatic alterations that drive cancer progression and patient survival. Although researchers have successfully leveraged TCGA data to build prognostic models, most efforts have focused on specific cancer types and a targeted set of gene-level predictors. Less is known about the prognostic ability of pathway-level variables in a pan-cancer setting. To address these limitations, we systematically evaluated and compared the prognostic ability of somatic point mutation (SPM) and copy number variation (CNV) data, gene-level and pathway-level models for a diverse set of TCGA cancer types and predictive modeling approaches.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We evaluated gene-level and pathway-level penalized Cox proportional hazards models using SPM and CNV data for 29 different TCGA cohorts. We measured predictive accuracy as the concordance index for predicting survival outcomes. Our comprehensive analysis suggests that the use of pathway-level predictors did not offer superior predictive power relative to gene-level models for all cancer types but had the advantages of robustness and parsimony. We identified a set of cohorts for which somatic alterations could not predict prognosis, and a unique cohort LGG, for which SPM data was more predictive than CNV data and the predictive accuracy is good for all model types. We found that the pathway-level predictors provide superior interpretative value and that there is often a serious collinearity issue for the gene-level models while pathway-level models avoided this issue.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our comprehensive analysis suggests that when using somatic alterations data for cancer prognosis prediction, pathway-level models are more interpretable, stable and parsimonious compared to gene-level models. Pathway-level models also avoid the issue of collinearity, which can be serious for gene-level somatic alterations. The prognostic power of somatic alterations is highly variable across different cancer types and we have identified a set of cohorts for which somatic alterations could not predict prognosis. In general, CNV data predicts prognosis better than SPM data with the exception of the LGG cohort.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03791-0","type":"journal-article","created":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T06:02:52Z","timestamp":1603173772000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Cancer prognosis prediction using somatic point mutation and copy number variation data: a comparison of gene-level and pathway-based models"],"prefix":"10.1186","volume":"21","author":[{"given":"Xingyu","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Christopher I.","family":"Amos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6794-9945","authenticated-orcid":false,"given":"H. Robert","family":"Frost","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,20]]},"reference":[{"key":"3791_CR1","unstructured":"The Cancer Genome Atlas Database. https:\/\/www.cancer.gov\/tcga. Accessed 2 Oct 2019."},{"issue":"8","key":"3791_CR2","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1001\/jama.2013.108415","volume":"310","author":"LJ Esserman","year":"2013","unstructured":"Esserman LJ, Thompson IM, Reid B. Overdiagnosis and overtreatment in cancer: an opportunity for improvement. J Am Med Assoc. 2013;310(8):797\u20138.","journal-title":"J Am Med Assoc"},{"issue":"3","key":"3791_CR3","doi-asserted-by":"publisher","first-page":"e1499","DOI":"10.7717\/peerj.1499","volume":"16","author":"J Anaya","year":"2016","unstructured":"Anaya J, Reon B, Chen WM, Bekiranov S, Dutta A. A pan-cancer analysis of prognostic genes. PeerJ. 2016;16(3):e1499.","journal-title":"PeerJ"},{"issue":"8","key":"3791_CR4","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1038\/nm.3909","volume":"21","author":"AJ Gentles","year":"2015","unstructured":"Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015;21(8):938\u201345.","journal-title":"Nat Med"},{"issue":"6352","key":"3791_CR5","doi-asserted-by":"publisher","first-page":"eaan2507","DOI":"10.1126\/science.aan2507","volume":"357","author":"M Uhlen","year":"2017","unstructured":"Uhlen M, Zhang C, Lee S, Sj\u00f6stedt E, Fagerberg L, Bidkhori G, et al. A pathology atlas of the human cancer transcriptome. Science. 2017;357(6352):eaan2507.","journal-title":"Science."},{"issue":"1","key":"3791_CR6","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1186\/s13073-019-0643-9","volume":"11","author":"P Little","year":"2019","unstructured":"Little P, Lin DY, Sun W. Associating somatic mutations to clinical outcomes: a pan-cancer study of survival time. Genome Med. 2019;11(1):37.","journal-title":"Genome Med"},{"key":"3791_CR7","doi-asserted-by":"publisher","first-page":"e37294","DOI":"10.7554\/eLife.37294","volume":"7","author":"H Hieronymus","year":"2018","unstructured":"Hieronymus H, Murali R, Tin A, Yadav K, Abida W, Moller H, et al. Tumor copy number alteration burden is a pan-cancer prognostic factor associated with recurrence and death. Elife. 2018;7:e37294.","journal-title":"Elife"},{"issue":"11","key":"3791_CR8","doi-asserted-by":"publisher","first-page":"e0207204","DOI":"10.1371\/journal.pone.0207204","volume":"13","author":"HJ Cho","year":"2018","unstructured":"Cho HJ, Lee S, Ji YG, Lee DH. Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma. PLoS ONE. 2018;13(11):e0207204.","journal-title":"PLoS ONE"},{"issue":"3","key":"3791_CR9","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/leu.2016.265","volume":"31","author":"O Nibourel","year":"2017","unstructured":"Nibourel O, Guihard S, Roumier C, Pottier N, Terre C, Paquet A, et al. Copy-number analysis identified new prognostic marker in acute myeloid leukemia. Leukemia. 2017;31(3):555\u201364.","journal-title":"Leukemia"},{"issue":"1","key":"3791_CR10","doi-asserted-by":"publisher","first-page":"14621","DOI":"10.1038\/s41598-017-14799-7","volume":"7","author":"M Kumaran","year":"2017","unstructured":"Kumaran M, Cass CE, Graham K, Mackey JR, Hubaux R, Lam W, et al. Germline copy number variations are associated with breast cancer risk and prognosis. Sci Rep. 2017;7(1):14621.","journal-title":"Sci Rep"},{"issue":"16","key":"3791_CR11","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1038\/onc.2015.304","volume":"35","author":"H Wang","year":"2016","unstructured":"Wang H, Liang L, Fang JY, Xu J. Somatic gene copy number alterations in colorectal cancer: new quest for cancer drivers and biomarkers. Oncogene. 2016;35(16):2011\u20139.","journal-title":"Oncogene"},{"issue":"2","key":"3791_CR12","doi-asserted-by":"publisher","first-page":"e1002375","DOI":"10.1371\/journal.pcbi.1002375","volume":"8","author":"P Khatri","year":"2012","unstructured":"Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2):e1002375.","journal-title":"PLoS Comput Biol"},{"issue":"43","key":"3791_CR13","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","volume":"102","author":"A Subramanian","year":"2005","unstructured":"Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545\u201350.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"17","key":"3791_CR14","doi-asserted-by":"publisher","first-page":"e133","DOI":"10.1093\/nar\/gks461","volume":"40","author":"D Wu","year":"2012","unstructured":"Wu D, Smyth GK. Camera: A competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 2012;40(17):e133.","journal-title":"Nucleic Acids Res"},{"issue":"7269","key":"3791_CR15","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/nature08460","volume":"462","author":"DA Barbie","year":"2009","unstructured":"Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462(7269):108\u201312.","journal-title":"Nature"},{"key":"3791_CR16","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/1471-2105-14-7","volume":"14","author":"S H\u00e4nzelmann","year":"2013","unstructured":"H\u00e4nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013;14:7.","journal-title":"BMC Bioinform"},{"key":"3791_CR17","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1186\/1471-2105-6-225","volume":"6","author":"J Tomfohr","year":"2005","unstructured":"Tomfohr J, Lu J, Kepler TB. Pathway level analysis of gene expression using singular value decomposition. BMC Bioinform. 2005;6:225.","journal-title":"BMC Bioinform"},{"key":"3791_CR18","unstructured":"The UCSC Xena Datahub. https:\/\/xena.ucsc.edu\/. Accessed 2 Oct 2019."},{"issue":"3","key":"3791_CR19","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.cels.2018.03.002","volume":"6","author":"K Ellrott","year":"2018","unstructured":"Ellrott K, Bailey MH, Saksena G, Covington KR, Kandoth C, Stewart C, et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 2018;6(3):271\u201381.","journal-title":"Cell Syst"},{"issue":"2","key":"3791_CR20","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.cell.2018.02.060","volume":"173","author":"MH Bailey","year":"2018","unstructured":"Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, et al. Comprehensive characterization of cancer driver genes and mutations. Cell. 2018;173(2):371\u201385.","journal-title":"Cell"},{"issue":"4","key":"3791_CR21","doi-asserted-by":"publisher","first-page":"R41","DOI":"10.1186\/gb-2011-12-4-r41","volume":"12","author":"CH Mermel","year":"2011","unstructured":"Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC20 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011;12(4):R41.","journal-title":"Genome Biol"},{"issue":"D1","key":"3791_CR22","doi-asserted-by":"publisher","first-page":"D941","DOI":"10.1093\/nar\/gky1015","volume":"47","author":"JG Tate","year":"2019","unstructured":"Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N, et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 2019;47(D1):D941\u20137.","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"3791_CR23","doi-asserted-by":"publisher","first-page":"e41","DOI":"10.5808\/GI.2019.17.4.e41","volume":"17","author":"S Lee","year":"2019","unstructured":"Lee S, Lim H. Review of statistical methods for survival analysis using genomic data. Genom Inform. 2019;17(4):e41.","journal-title":"Genom Inform"},{"issue":"2","key":"3791_CR24","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/S0167-9473(99)00098-5","volume":"34","author":"A Xiang","year":"2000","unstructured":"Xiang A, Lapuerta P, Ryutov A, Buckley J, Azen S. Comparison of the performance of neural network methods and Cox regression for censored survival data. Comput Stat Data Anal. 2000;34(2):243\u201357.","journal-title":"Comput Stat Data Anal"},{"issue":"6 Pt 2","key":"3791_CR25","first-page":"S6","volume":"170","author":"MW Kattan","year":"2003","unstructured":"Kattan MW, Kantoff PW, Nelson JB, Carroll PR, Roach M, Higano CS. Comparison of Cox regression with other methods for determining prediction models and nomograms. J Urol. 2003;170(6 Pt 2):S6-10.","journal-title":"J Urol"},{"issue":"11","key":"3791_CR26","doi-asserted-by":"publisher","first-page":"R112","DOI":"10.1186\/gb-2010-11-11-r112","volume":"11","author":"SM Boca","year":"2010","unstructured":"Boca SM, Kinzler KW, Velculescu VE, Vogelstein B, Parmigiani G. Patient-oriented gene set analysis for cancer mutation data. Genome Biol. 2010;11(11):R112.","journal-title":"Genome Biol"},{"issue":"76","key":"3791_CR27","first-page":"1","volume":"21","author":"X Zheng","year":"2020","unstructured":"Zheng X, Amos CI, Frost HR. Comparison of pathway and gene-level models for cancer prognosis prediction. BMC Bioinform. 2020;21(76):1\u20137.","journal-title":"BMC Bioinform"},{"issue":"18","key":"3791_CR28","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","volume":"247","author":"FE Harrell","year":"1982","unstructured":"Harrell FE. Evaluating the yield of medical tests. J Am Med Assoc. 1982;247(18):2543\u20136.","journal-title":"J Am Med Assoc"},{"issue":"5","key":"3791_CR29","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1037\/h0031619","volume":"76","author":"JL Fleiss","year":"1971","unstructured":"Fleiss JL. Measuring nominal scale agreement among many raters. Psychol Bull. 1971;76(5):378\u201382.","journal-title":"Psychol Bull"},{"issue":"3","key":"3791_CR30","doi-asserted-by":"publisher","first-page":"276","DOI":"10.11613\/BM.2012.031","volume":"22","author":"ML McHugh","year":"2012","unstructured":"McHugh ML. Interrater reliability: the kappa statistic. Biochem Medica. 2012;22(3):276\u201382.","journal-title":"Biochem Medica"},{"key":"3791_CR31","doi-asserted-by":"crossref","unstructured":"Jardillier R, Guyon L. Benchmark of lasso-like penalties in the Cox model for TCGA datasets reveal improved performance with pre-filtering and wide differences between cancers. bioRxiv Bioinforma. 2020.","DOI":"10.1101\/2020.03.09.984070"},{"issue":"2","key":"3791_CR32","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.cell.2018.02.052","volume":"173","author":"J Liu","year":"2018","unstructured":"Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell. 2018;173(2):400\u201316.","journal-title":"Cell"},{"issue":"9","key":"3791_CR33","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1593\/neo.10688","volume":"12","author":"KJ Hatanpaa","year":"2010","unstructured":"Hatanpaa KJ, Burma S, Zhao D, Habib AA. Epidermal growth factor receptor in glioma: signal transduction, neuropathology, imaging, and radioresistance. Neoplasia. 2010;12(9):675\u201384.","journal-title":"Neoplasia"},{"key":"3791_CR34","doi-asserted-by":"publisher","first-page":"9043","DOI":"10.1038\/s41598-017-08940-9","volume":"7","author":"D Chakravarty","year":"2017","unstructured":"Chakravarty D, Pedraza AM, Cotari J, Liu AH, Punko D, Kokroo A, et al. EGFR and PDGFRA co-expression and heterodimerization in glioblastoma tumor sphere lines. Sci Rep. 2017;7:9043.","journal-title":"Sci Rep"},{"issue":"12","key":"3791_CR35","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1158\/1541-7786.MCR-06-0085","volume":"4","author":"M Puputti","year":"2006","unstructured":"Puputti M, Tynninen O, Sihto H, Blom T, M\u00e4enp\u00e4\u00e4 H, Isola J, et al. Amplification of KIT, PDGFRA, VEGFR2, and EGFR in gliomas. Mol Cancer Res. 2006;4(12):927\u201334.","journal-title":"Mol Cancer Res"},{"key":"3791_CR36","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1093\/neuonc\/not087","volume":"15","author":"C Zhang","year":"2013","unstructured":"Zhang C, Moore LM, Li X, Yung WKA, Zhang W. IDH1\/2 mutations target a key hallmark of cancer by deregulating cellular metabolism in glioma. Neuro-Oncology. 2013;15:1114\u201326.","journal-title":"Neuro-Oncology"},{"key":"3791_CR37","unstructured":"Therneau T. A Package for Survival Analysis in R. R package version 3.1-12. 2020. https:\/\/cran.r-project.org\/package=survival. Accessed 2 Oct 2019."},{"key":"3791_CR38","volume-title":"Analysis of biological systems","author":"P Corrado","year":"2015","unstructured":"Corrado P, Melissa JM. Analysis of biological systems. London: Imperial College Press; 2015."},{"issue":"1","key":"3791_CR39","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s00726-016-2342-9","volume":"49","author":"A Maus","year":"2017","unstructured":"Maus A, Peters GJ. Glutamate and \u03b1-ketoglutarate: key players in glioma metabolism. Amino Acids. 2017;49(1):21\u201332.","journal-title":"Amino Acids"},{"key":"3791_CR40","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1159\/000186689","volume":"17","author":"N Mizuno","year":"2009","unstructured":"Mizuno N, Itoh H. Functions and regulatory mechanisms of Gq-signaling pathways. Neurosignals. 2009;17:42\u201354.","journal-title":"Neurosignals"},{"key":"3791_CR41","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.neuroscience.2014.08.015","volume":"10","author":"AE Cherry","year":"2014","unstructured":"Cherry AE, Stella N. G protein-coupled receptors as oncogenic signals in glioma: emerging therapeutic avenues. Neuroscience. 2014;10:222\u201336.","journal-title":"Neuroscience"},{"issue":"287","key":"3791_CR42","doi-asserted-by":"publisher","first-page":"1960","DOI":"10.1126\/science.287.5460.1960","volume":"80","author":"J Drews","year":"2000","unstructured":"Drews J. Drug discovery: a historical perspective. Science (80-). 2000;80(287):1960\u20134.","journal-title":"Science (80-)"},{"issue":"70","key":"3791_CR43","doi-asserted-by":"publisher","first-page":"115736","DOI":"10.18632\/oncotarget.22803","volume":"8","author":"JP Phelan","year":"2017","unstructured":"Phelan JP, Reen FJ, Caparros-Martin JA, O\u2019Connor R, O\u2019Gara F. Rethinking the bile acid\/gut microbiome axis in cancer. Oncotarget. 2017;8(70):115736\u201347.","journal-title":"Oncotarget"},{"key":"3791_CR44","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-1-60327-492-0_10","volume":"472","author":"MS Singh","year":"2009","unstructured":"Singh MS, Michael M. Role of xenobiotic metabolic enzymes in cancer epidemiology. Methods Mol Biol. 2009;472:243\u201364.","journal-title":"Methods Mol Biol"},{"issue":"8","key":"3791_CR45","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1056\/NEJMoa0808710","volume":"360","author":"H Yan","year":"2009","unstructured":"Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360(8):765\u201373.","journal-title":"N Engl J Med"},{"issue":"1","key":"3791_CR46","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1111\/j.1600-0587.2012.07348.x","volume":"36","author":"CF Dormann","year":"2013","unstructured":"Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carr\u00e9 G, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop). 2013;36(1):27\u201346.","journal-title":"Ecography (Cop)"},{"issue":"11","key":"3791_CR47","first-page":"2541","volume":"7","author":"P Zhao","year":"2006","unstructured":"Zhao P, Yu B. On model selection consistency of Lasso. J Mach Learn Res. 2006;7(11):2541\u201363.","journal-title":"J Mach Learn Res"},{"issue":"476","key":"3791_CR48","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1198\/016214506000000735","volume":"101","author":"H Zou","year":"2006","unstructured":"Zou H. The adaptive lasso and its oracle properties. J Am Stat Assoc. 2006;101(476):1418\u201329.","journal-title":"J Am Stat Assoc"},{"key":"3791_CR49","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s10463-012-0370-0","volume":"65","author":"W Qian","year":"2013","unstructured":"Qian W, Yang Y. Model selection via standard error adjusted adaptive lasso. Ann Inst Stat Math. 2013;65:295\u2013318.","journal-title":"Ann Inst Stat Math"},{"issue":"7","key":"3791_CR50","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1093\/bioinformatics\/bty750","volume":"35","author":"H Wang","year":"2019","unstructured":"Wang H, Lengerich BJ, Aragam B, Xing EP. Precision Lasso: Accounting for correlations and linear dependencies in high-dimensional genomic data. Bioinformatics. 2019;35(7):1181\u20137.","journal-title":"Bioinformatics"},{"issue":"23\u201324","key":"3791_CR51","doi-asserted-by":"publisher","first-page":"2427","DOI":"10.1002\/sim.4780132307","volume":"13","author":"PJM Verweij","year":"1994","unstructured":"Verweij PJM, Van Houwelingen HC. Penalized likelihood in Cox regression. Stat Med. 1994;13(23\u201324):2427\u201336.","journal-title":"Stat Med"},{"issue":"2","key":"3791_CR52","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67(2):301\u201320.","journal-title":"J R Stat Soc Ser B Stat Methodol"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03791-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-020-03791-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03791-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T23:37:19Z","timestamp":1634686639000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03791-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,20]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3791"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03791-0","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2020,10,20]]},"assertion":[{"value":"15 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2020","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"}}],"article-number":"467"}}