{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T03:01:16Z","timestamp":1767927676768,"version":"3.49.0"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"1","funder":[{"name":"European Research Council","award":["617393"],"award-info":[{"award-number":["617393"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1186\/s12859-018-2023-7","type":"journal-article","created":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T00:54:20Z","timestamp":1516668860000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Feature selection for high-dimensional temporal data"],"prefix":"10.1186","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2049-3063","authenticated-orcid":false,"given":"Michail","family":"Tsagris","sequence":"first","affiliation":[]},{"given":"Vincenzo","family":"Lagani","sequence":"additional","affiliation":[]},{"given":"Ioannis","family":"Tsamardinos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,1,23]]},"reference":[{"key":"2023_CR1","unstructured":"Tsamardinos I, Aliferis CF, Statnikov AR, Statnikov E. Algorithms for Large Scale Markov Blanket Discovery. In: FLAIRS Conference, vol. 2: 2003. p. 376\u2013381."},{"issue":"1","key":"2023_CR2","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s10994-006-6889-7","volume":"65","author":"I Tsamardinos","year":"2006","unstructured":"Tsamardinos I, Brown LE, Aliferis CF. The Max-Min Hill-Climbing Bayesian network structure learning algorithm. Mach Learn. 2006; 65(1):31\u201378.","journal-title":"Mach Learn"},{"key":"2023_CR3","volume-title":"Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"I Tsamardinos","year":"2003","unstructured":"Tsamardinos I, Aliferis CF, Statnikov A. Time and sample efficient discovery of Markov Blankets and direct causal relations. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM: 2003. p. 673\u20138."},{"key":"2023_CR4","first-page":"171","volume":"11","author":"CF Aliferis","year":"2010","unstructured":"Aliferis CF, Statnikov AR, Tsamardinos I, Mani S, Koutsoukos XD. Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I : Algorithms and Empirical Evaluation. J Mach Learn Res. 2010; 11:171\u2013234.","journal-title":"J Mach Learn Res"},{"key":"2023_CR5","doi-asserted-by":"crossref","unstructured":"Lagani V, Athineou G, Farcomeni A, Tsagris M, Tsamardinos I. Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets. J Stat Softw. 2017; 80.","DOI":"10.18637\/jss.v080.i07"},{"key":"2023_CR6","volume-title":"Learning Bayesian Networks","author":"RE Neapolitan","year":"2004","unstructured":"Neapolitan RE. Learning Bayesian Networks. Upper Saddle River: Prentice Hall; 2004."},{"issue":"15","key":"2023_CR7","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1093\/bioinformatics\/btq261","volume":"26","author":"V Lagani","year":"2010","unstructured":"Lagani V, Tsamardinos I. Structure-based variable selection for survival data. Bioinformatics. 2010; 26(15):1887\u201394.","journal-title":"Bioinformatics"},{"key":"2023_CR8","volume-title":"Statistical Inference. 2nd Ed","author":"G Casella","year":"2002","unstructured":"Casella G, Berger R. Statistical Inference. 2nd Ed. Pacific Grove: Duxbury Press; 2002."},{"key":"2023_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-0318-1","volume-title":"Mixed-effects Models in S and S-PLUS","author":"J Pinheiro","year":"2000","unstructured":"Pinheiro J, Bates D. Mixed-effects Models in S and S-PLUS. New York: Springer; 2000."},{"key":"2023_CR10","unstructured":"Tsamardinos I, Lagani V, Pappas D. Discovering multiple, equivalent biomarker signatures. In: Proceedings of the 7th Conference of the Hellenic Society for Computational Biology & Bioinformatics. 54\u201356: 2012."},{"key":"2023_CR11","first-page":"235","volume":"11","author":"CF Aliferis","year":"2010","unstructured":"Aliferis CF, Statnikov A, Tsamardinos I, Mani S, Koutsoukos XD. Local causal and Markov Blanket induction for causal discovery and feature selection for classification part ii: Analysis and extensions. J Mach Learn Res. 2010; 11:235\u201384.","journal-title":"J Mach Learn Res"},{"issue":"Mar","key":"2023_CR12","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003; 3(Mar):1157\u201382.","journal-title":"J Mach Learn Res"},{"key":"2023_CR13","doi-asserted-by":"crossref","unstructured":"Pavlidis P, Weston J, Cai J, Grundy WN. Gene functional classification from heterogeneous data. In: Proceedings of the Fifth Annual International Conference on Computational Biology. ACM: 2001. p. 249\u201355.","DOI":"10.1145\/369133.369228"},{"key":"2023_CR14","volume-title":"International Conference on Neural Information Processing","author":"MW Mak","year":"2006","unstructured":"Mak MW, Kung SY. A solution to the curse of dimensionality problem in pairwise scoring techniques. In: International Conference on Neural Information Processing. Berlin, Heidelberg: Springer: 2006. p. 314\u201323."},{"issue":"19","key":"2023_CR15","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys Y, Inza I, Larra\u00f1aga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007; 23(19):2507\u201317.","journal-title":"Bioinformatics"},{"issue":"4","key":"2023_CR16","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1109\/TCBB.2008.138","volume":"7","author":"X Lu","year":"2010","unstructured":"Lu X, Gamst A, Xu R. RDCurve: A nonparametric method to evaluate the stability of ranking procedures. IEEE\/ACM Trans Comput Biol Bioinforma (TCBB). 2010; 7(4):719\u201326.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma (TCBB)"},{"issue":"3","key":"2023_CR17","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1109\/TNB.2012.2214232","volume":"11","author":"Q Wu","year":"2012","unstructured":"Wu Q, Ye Y, Liu Y, Ng MK. Snp selection and classification of genome-wide snp data using stratified sampling random forests. IEEE Trans Nanobioscience. 2012; 11(3):216\u201327.","journal-title":"IEEE Trans Nanobioscience"},{"issue":"3","key":"2023_CR18","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1016\/j.patcog.2012.09.005","volume":"46","author":"Y Ye","year":"2013","unstructured":"Ye Y, Wu Q, Huang JZ, Ng MK, Li X. Stratified sampling for feature subspace selection in random forests for high dimensional data. Pattern Recogn. 2013; 46(3):769\u201387.","journal-title":"Pattern Recogn"},{"key":"2023_CR19","volume-title":"Innovations in Bio-Inspired Computing and Applications","author":"A Chinnaswamy","year":"2016","unstructured":"Chinnaswamy A, Srinivasan R. Hybrid Feature Selection Using Correlation Coefficient and Particle Swarm Optimization on Microarray Gene Expression Data. In: Innovations in Bio-Inspired Computing and Applications. Cham: Springer: 2016. p. 229\u201339."},{"key":"2023_CR20","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.jtbi.2016.03.034","volume":"400","author":"S Guo","year":"2016","unstructured":"Guo S, Guo D, Chen L, Jiang Q. A centroid-based gene selection method for microarray data classification. J Theor Biol. 2016; 400:32\u201341.","journal-title":"J Theor Biol"},{"issue":"1","key":"2023_CR21","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1186\/s12859-016-0954-4","volume":"17","author":"MF Ghalwash","year":"2016","unstructured":"Ghalwash MF, Cao XH, Stojkovic I, Obradovic Z. Structured feature selection using coordinate descent optimization. BMC Bioinformatics. 2016; 17(1):158.","journal-title":"BMC Bioinformatics"},{"issue":"6","key":"2023_CR22","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.ygeno.2016.05.001","volume":"107","author":"FV Sharbaf","year":"2016","unstructured":"Sharbaf FV, Mosafer S, Moattar MH. A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. Genomics. 2016; 107(6):231\u20138.","journal-title":"Genomics"},{"issue":"4","key":"2023_CR23","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s11390-016-1656-0","volume":"31","author":"C Han","year":"2016","unstructured":"Han C, Tan YK, Zhu JH, Guo Y, Chen J, Wu QY. Online feature selection of class imbalance via pa algorithm. J Comput Sci Technol. 2016; 31(4):673\u201382.","journal-title":"J Comput Sci Technol"},{"key":"2023_CR24","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"1984","unstructured":"Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and Regression Trees. Belmont: CRC press; 1984."},{"issue":"5439","key":"2023_CR25","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999; 286(5439):531\u20137.","journal-title":"Science"},{"issue":"2","key":"2023_CR26","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s13748-015-0080-y","volume":"5","author":"V Bol\u00f3n-Canedo","year":"2016","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A. Feature selection for high-dimensional data. Progress Artif Intell. 2016; 5(2):65\u201375.","journal-title":"Progress Artif Intell"},{"issue":"2","key":"2023_CR27","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11222-012-9359-z","volume":"24","author":"A Groll","year":"2014","unstructured":"Groll A, Tutz G. Variable selection for generalized linear mixed models by L1-penalized estimation. Stat Comput. 2014; 24(2):137\u201354.","journal-title":"Stat Comput"},{"issue":"12","key":"2023_CR28","doi-asserted-by":"publisher","first-page":"3304","DOI":"10.1016\/j.csda.2011.06.016","volume":"55","author":"H Matsui","year":"2011","unstructured":"Matsui H, Konishi S. Variable selection for functional regression models via the L1 regularization. Comput Stat Data Anal. 2011; 55(12):3304\u201310.","journal-title":"Comput Stat Data Anal"},{"issue":"1","key":"2023_CR29","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1002\/sta4.20","volume":"2","author":"J Gertheiss","year":"2013","unstructured":"Gertheiss J, Maity A, Staicu AM. Variable selection in generalized functional linear models. Stat. 2013; 2(1):86\u2013101.","journal-title":"Stat"},{"issue":"2","key":"2023_CR30","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1093\/biostatistics\/kxv037","volume":"17","author":"M Kayano","year":"2015","unstructured":"Kayano M, Matsui H, Yamaguchi R, Imoto S, Miyano S. Gene set differential analysis of time course expression profiles via sparse estimation in functional logistic model with application to time-dependent biomarker detection. Biostatistics. 2015; 17(2):235\u2013248.","journal-title":"Biostatistics"},{"issue":"1","key":"2023_CR31","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1111\/j.1541-0420.2009.01240.x","volume":"66","author":"X Ni","year":"2010","unstructured":"Ni X, Zhang D, Zhang HH. Variable selection for semiparametric mixed models in longitudinal studies. Biometrics. 2010; 66(1):79\u201388.","journal-title":"Biometrics"},{"issue":"4","key":"2023_CR32","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1111\/j.1541-0420.2010.01391.x","volume":"66","author":"HD Bondell","year":"2010","unstructured":"Bondell HD, Krishna A, Ghosh SK. Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models. Biometrics. 2010; 66(4):1069\u201377.","journal-title":"Biometrics"},{"issue":"2","key":"2023_CR33","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1111\/j.1541-0420.2010.01463.x","volume":"67","author":"JG Ibrahim","year":"2011","unstructured":"Ibrahim JG, Zhu H, Garcia RI, Guo R. Fixed and random effects selection in mixed effects models. Biometrics. 2011; 67(2):495\u2013503.","journal-title":"Biometrics"},{"issue":"1","key":"2023_CR34","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s10463-010-0312-7","volume":"64","author":"P Zhao","year":"2012","unstructured":"Zhao P, Xue L. Variable selection in semiparametric regression analysis for longitudinal data. Ann Inst Stat Math. 2012; 64(1):213\u201331.","journal-title":"Ann Inst Stat Math"},{"issue":"1","key":"2023_CR35","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.csda.2012.07.015","volume":"57","author":"Y Tang","year":"2013","unstructured":"Tang Y, Wang HJ, Zhu Z. Variable selection in quantile varying coefficient models with longitudinal data. Comput Stat Data Anal. 2013; 57(1):435\u201349.","journal-title":"Comput Stat Data Anal"},{"issue":"2","key":"2023_CR36","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1111\/j.1467-9469.2011.00740.x","volume":"38","author":"J Schelldorfer","year":"2011","unstructured":"Schelldorfer J, B\u00fchlmann P, Van De Geer S. Estimation for High-Dimensional Linear Mixed-Effects Models Using l1-Penalization. Scand J Stat. 2011; 38(2):197\u2013214. Wiley Online Library.","journal-title":"Scand J Stat"},{"issue":"2","key":"2023_CR37","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1080\/10618600.2013.773239","volume":"23","author":"J Schelldorfer","year":"2014","unstructured":"Schelldorfer J, Meier L, B\u00fchlmann P. Glmmlasso: an algorithm for high-dimensional generalized linear mixed models using l1-penalization. J Comput Graph Stat. 2014; 23(2):460\u201377.","journal-title":"J Comput Graph Stat"},{"issue":"2","key":"2023_CR38","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1214\/12-STS410","volume":"28","author":"S M\u00fcller","year":"2013","unstructured":"M\u00fcller S, Scealy JL, Welsh AH. Model selection in linear mixed models. Stat Sci. 2013; 28(2):135\u201367.","journal-title":"Stat Sci"},{"issue":"1","key":"2023_CR39","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1111\/j.0006-341X.2001.00120.x","volume":"57","author":"W Pan","year":"2001","unstructured":"Pan W. Akaike\u2019s information criterion in generalized estimating equations. Biometrics. 2001; 57(1):120\u20135.","journal-title":"Biometrics"},{"issue":"2","key":"2023_CR40","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1111\/j.1541-0420.2005.00331.x","volume":"61","author":"E Cantoni","year":"2005","unstructured":"Cantoni E, Flemming JM, Ronchetti E. Variable selection for marginal longitudinal generalized linear models. Biometrics. 2005; 61(2):507\u201314.","journal-title":"Biometrics"},{"issue":"4","key":"2023_CR41","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1002\/sim.2572","volume":"26","author":"E Cantoni","year":"2007","unstructured":"Cantoni E, Field C, Mills Flemming J, Ronchetti E. Longitudinal variable selection by cross-validation in the case of many covariates. Stat Med. 2007; 26(4):919\u201330.","journal-title":"Stat Med"},{"issue":"4","key":"2023_CR42","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1111\/j.1541-0420.2012.01758.x","volume":"68","author":"CW Shen","year":"2012","unstructured":"Shen CW, Chen YH. Model selection for generalized estimating equations accommodating dropout missingness. Biometrics. 2012; 68(4):1046\u201354.","journal-title":"Biometrics"},{"issue":"2","key":"2023_CR43","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1111\/j.1541-0420.2011.01678.x","volume":"68","author":"L Wang","year":"2012","unstructured":"Wang L, Zhou J, Qu A. Penalized Generalized Estimating Equations for High-Dimensional Longitudinal Data Analysis. Biometrics. 2012; 68(2):353\u201360.","journal-title":"Biometrics"},{"issue":"4","key":"2023_CR44","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1111\/j.0006-341X.2003.00089.x","volume":"59","author":"Z Chen","year":"2003","unstructured":"Chen Z, Dunson DB. Random effects selection in linear mixed models. Biometrics. 2003; 59(4):762\u20139.","journal-title":"Biometrics"},{"issue":"455","key":"2023_CR45","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1198\/016214501753208780","volume":"96","author":"C Han","year":"2001","unstructured":"Han C, Carlin BP. Markov chain Monte Carlo methods for computing Bayes factors: A comparative review. J Am Stat Assoc. 2001; 96(455):1122\u201332.","journal-title":"J Am Stat Assoc"},{"issue":"4","key":"2023_CR46","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1111\/1467-9868.00353","volume":"64","author":"DJ Spiegelhalter","year":"2002","unstructured":"Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Ser B (Stat Methodol). 2002; 64(4):583\u2013639.","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"issue":"2","key":"2023_CR47","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1111\/j.1541-0420.2008.01107.x","volume":"65","author":"BR Saville","year":"2009","unstructured":"Saville BR, Herring AH. Testing random effects in the linear mixed model using approximate Bayes factors. Biometrics. 2009; 65(2):369\u201376.","journal-title":"Biometrics"},{"key":"2023_CR48","doi-asserted-by":"crossref","unstructured":"Lix LM, Sajobi TT. Discriminant analysis for repeated measures data: a review. Front Psychol. 2010; 1.","DOI":"10.3389\/fpsyg.2010.00146"},{"key":"2023_CR49","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.csda.2014.04.015","volume":"78","author":"H Matsui","year":"2014","unstructured":"Matsui H. Variable and boundary selection for functional data via multiclass logistic regression modeling. Comput Stat Data Anal. 2014; 78:176\u201385.","journal-title":"Comput Stat Data Anal"},{"issue":"4","key":"2023_CR50","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1093\/biomet\/asq058","volume":"97","author":"F Ferraty","year":"2010","unstructured":"Ferraty F, Hall P, Vieu P. Most-predictive design points for functional data predictors. Biometrika. 2010; 97(4):807\u201324.","journal-title":"Biometrika"},{"issue":"1","key":"2023_CR51","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 R Stat Soc Ser B (Methodol). 1996; 58(1):267\u201388.","journal-title":"J R Stat Soc Ser B (Methodol)"},{"issue":"2","key":"2023_CR52","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","volume":"32","author":"B Efron","year":"2004","unstructured":"Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. Ann Stat. 2004; 32(2):407\u201399.","journal-title":"Ann Stat"},{"issue":"1","key":"2023_CR53","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1214\/07-AOAS147","volume":"2","author":"TT Wu","year":"2008","unstructured":"Wu TT, Lange K. Coordinate descent algorithms for lasso penalized regression. Ann Appl Stat. 2008; 2(1):224\u201344.","journal-title":"Ann Appl Stat"},{"issue":"1","key":"2023_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010; 33(1):1\u201322.","journal-title":"J Stat Softw"},{"issue":"1","key":"2023_CR55","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 (Stat Methodol). 2006; 68(1):49\u201367.","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"issue":"6","key":"2023_CR56","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1007\/s11222-014-9498-5","volume":"25","author":"Y Yang","year":"2015","unstructured":"Yang Y, Zou H. A fast unified algorithm for solving group-lasso penalize learning problems. Stat Comput. 2015; 25(6):1129\u201341.","journal-title":"Stat Comput"},{"key":"2023_CR57","unstructured":"Yang Y, Zou H. gglasso: Group Lasso Penalized Learning Using A Unified BMD Algorithm. 2014. R package version 1.3. http:\/\/CRAN.R-project.org\/package=gglasso ."},{"issue":"2","key":"2023_CR58","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1002\/sim.3478","volume":"28","author":"JC Gardiner","year":"2009","unstructured":"Gardiner JC, Luo Z, Roman LA. Fixed effects, random effects and GEE: what are the differences?. Stat Med. 2009; 28(2):221\u201339.","journal-title":"Stat Med"},{"issue":"4","key":"2023_CR59","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1097\/EDE.0b013e3181caeb90","volume":"21","author":"AE Hubbard","year":"2010","unstructured":"Hubbard AE, Ahern J, Fleischer NL, Van der Laan M, Lippman SA, Jewell N, Bruckner T, Satariano WA. To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology. 2010; 21(4):467\u201374.","journal-title":"Epidemiology"},{"issue":"1","key":"2023_CR60","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1093\/biomet\/73.1.13","volume":"73","author":"KY Liang","year":"1986","unstructured":"Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986; 73(1):13\u201322.","journal-title":"Biometrika"},{"issue":"4","key":"2023_CR61","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.2307\/2531734","volume":"44","author":"SL Zeger","year":"1988","unstructured":"Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988; 44(4):1049\u201360.","journal-title":"Biometrics"},{"issue":"4","key":"2023_CR62","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1080\/03610918808812718","volume":"17","author":"MC Paik","year":"1988","unstructured":"Paik MC. Repeated measurement analysis for nonnormal data in small samples. Commun Stat-Simul Comput. 1988; 17(4):1155\u201371.","journal-title":"Commun Stat-Simul Comput"},{"issue":"24","key":"2023_CR63","doi-asserted-by":"publisher","first-page":"3345","DOI":"10.1002\/1097-0258(20001230)19:24<3345::AID-SIM829>3.0.CO;2-5","volume":"19","author":"A Ziegler","year":"2000","unstructured":"Ziegler A, Kastner C, Brunner D, Blettner M. Familial associations of lipid profiles: A generalised estimating equations approach. Stat Med. 2000; 19(24):3345\u201357.","journal-title":"Stat Med"},{"issue":"6","key":"2023_CR64","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1002\/sim.1650","volume":"23","author":"J Yan","year":"2004","unstructured":"Yan J, Fine J. Estimating equations for association structures. Stat Med. 2004; 23(6):859\u201374.","journal-title":"Stat Med"},{"issue":"1","key":"2023_CR65","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1080\/00031305.2000.10474509","volume":"54","author":"Y Pawitan","year":"2000","unstructured":"Pawitan Y. A reminder of the fallibility of the wald statistic: likelihood explanation. Am Stat. 2000; 54(1):54\u20136.","journal-title":"Am Stat"},{"issue":"1","key":"2023_CR66","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/0025-5564(72)90016-8","volume":"14","author":"S Azen","year":"1972","unstructured":"Azen S, Afifi AA. Two models for assessing prognosis on the basis of successive observations. Math Biosci. 1972; 14(1):169\u201376.","journal-title":"Math Biosci"},{"issue":"9","key":"2023_CR67","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1093\/bioinformatics\/btl056","volume":"22","author":"A Conesa","year":"2006","unstructured":"Conesa A, Nueda MJ, Ferrer A, Tal\u00f3n M. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics. 2006; 22(9):1096\u2013102.","journal-title":"Bioinformatics"},{"issue":"5","key":"2023_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1000790","volume":"6","author":"A Statnikov","year":"2010","unstructured":"Statnikov A, Aliferis CF. Analysis and Computational Dissection of Molecular Signature Multiplicity. PLoS Comput Biol. 2010; 6(5):1\u20139. https:\/\/doi.org\/10.1371\/journal.pcbi.1000790 .","journal-title":"PLoS Comput Biol"},{"key":"2023_CR69","doi-asserted-by":"publisher","DOI":"10.1007\/b98886","volume-title":"Applied Functional Data Analysis: Methods and Case Studies","author":"JO Ramsay","year":"2002","unstructured":"Ramsay JO, Silverman BW. Applied Functional Data Analysis: Methods and Case Studies. New York: Springer; 2002."},{"issue":"2","key":"2023_CR70","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1214\/08-AOAS224","volume":"3","author":"RJ Tibshirani","year":"2009","unstructured":"Tibshirani RJ, Tibshirani R. A bias correction for the minimum error rate in cross-validation. Ann Appl Stat. 2009; 3(2):822\u20139.","journal-title":"Ann Appl Stat"},{"key":"2023_CR71","volume-title":"Causation, Prediction, and Search","author":"P Spirtes","year":"2000","unstructured":"Spirtes P, Glymour CN, Scheines R. Causation, Prediction, and Search. Cambridge: MIT press; 2000."},{"issue":"1","key":"2023_CR72","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/28.1.27","volume":"28","author":"M Kanehisa","year":"2000","unstructured":"Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000; 28(1):27\u201330.","journal-title":"Nucleic Acids Res"},{"key":"2023_CR73","doi-asserted-by":"crossref","unstructured":"Tsamardinos I, Rakhshani A, Lagani V. Performance-Estimation Properties of Cross-Validation-Based Protocols with Simultaneous Hyper-Parameter Optimization. 2014;:1\u201314.","DOI":"10.1007\/978-3-319-07064-3_1"},{"issue":"5","key":"2023_CR74","doi-asserted-by":"publisher","first-page":"1540023","DOI":"10.1142\/S0218213015400230","volume":"24","author":"I Tsamardinos","year":"2015","unstructured":"Tsamardinos I, Rakhshani A, Lagani V. Performance-Estimation Properties of Cross-Validation-Based Protocols with Simultaneous Hyper-Parameter Optimization. Int J Artif Intell Tools. 2015; 24(5):1540023.","journal-title":"Int J Artif Intell Tools"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-018-2023-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T17:33:13Z","timestamp":1719768793000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-018-2023-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,23]]},"references-count":74,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["2023"],"URL":"https:\/\/doi.org\/10.1186\/s12859-018-2023-7","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,23]]},"assertion":[{"value":"17 July 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2018","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":"17"}}