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Том 18   Выпуск 2   Год 2023
Список литературы

  1. Cockeran M., Meintanis S.G., Allison J.S. Goodness-of-fit tests in the Cox proportional hazards model. Communications in Statistics – Simulation and Computation. 2019;50(12):4132–4143. doi: 10.1007/978-1-4612-0103-8_18
  2. Emura T., Chen Y.H., Chen, H.Y. Survival prediction based on compound covariate under Cox proportional hazard models. PLoS One. 2012;7(10). Article No. e47627. doi: 10.1371/journal.pone.0047627
  3. Huang J., Liu L., Liu Y., Zhao X. Group selection in the Cox model with a diverging number of covariates. Statistica Sinica. 2014:1787–1810. doi: 10.5705/ss.2013.061
  4. Karabey U., Tutkun N.A. Model selection criterion in survival analysis. AIP Conference Proceedings. 2017;1863(1). Article No. 120003. doi: 10.1063/1.4992296
  5. Leng C., Zhang H.H. Model selection in nonparametric hazard regression. Journal of Nonparametric Statistics. 2006;18(7–8):417–429. doi: 10.1080/10485250601027042
  6. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 1996;58(1):267–288. doi: 10.1111/j.2517-6161.1996.tb02080.x
  7. Fan J., Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association. 2001;96(456):1348–1360. doi: 10.1198/016214501753382273
  8. Zou H., Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2005;67(2):301–320. doi: 10.1111/j.1467-9868.2005.00503.x
  9. Zou H. The adaptive lasso and its oracle properties. Journal of the American Statistical Association. 2006;101(476):1418–1429. doi: 10.1198/016214506000000735
  10. Algamal Z.Y., Lee M.H. Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert Systems with Applications. 2015;42(23):9326–9332. doi: 10.1016/j.eswa.2015.08.016
  11. Algamal Z.Y., Lee M.H. Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification. Computers in Biology and Medicine. 2015;67:136–145. doi: 10.1016/j.compbiomed.2015.10.008
  12. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286(5439):531–537. doi: 10.1126/science.286.5439.531
  13. Nguyen D.V., Rocke D.M. Tumor classification by partial least squares using microarray gene expression data. Bioinformatics. 2002;18(1):39–50. doi: 10.1093/bioinformatics/18.1.39
  14. Xiong M., Jin L., Li W., Boerwinkle E. Computational methods for gene expression-based tumor classification. Biotechniques. 2000;29(6):264–1270. doi: 10.2144/00296bc02
  15. Baldi P., Long A.D. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics. 2001;17(6):509–519. doi: 10.1093/bioinformatics/17.6.509
  16. Shibly F.H.A., Kumar R.L. Image Processing for Automatic Cell Nucleus Segmentation Using Super pixel and Clustering Methods on Histopathological Images. Tamjeed Journal of Healthcare Engineering and Science Technology. 2023;1(1):54–63. doi: 10.59785/tjhest.v1i1.6
  17. Statnikov A., Aliferis C.F., Tsamardinos I., Hardin D., Levy S. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics. 2005;21(5):631–643. doi: 10.1093/bioinformatics/bti033
  18. Liu Y. Detect key gene information in classification of microarray data. EURASIP Journal on Advances in Signal Processing. 2008. Article No. 612397. doi: 10.1155/2008/612397
  19. Cox D.R. Regression models and life‐tables. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 1972;34(2):187–202. doi: 10.1111/j.2517-6161.1972.tb00899.x
  20. Du P., Ma S., Liang H. Penalized Variable Selection Procedure for Cox Models with Semiparametric Relative Risk. Ann. Stat. 2010;38(4):2092–2117. doi: 10.1214/09-AOS780
  21. Fu Z., Parikh C.R., Zhou B. Penalized variable selection in competing risks regression. Lifetime Data Anal. 2017;23(3):353–376. doi: 10.1007/s10985-016-9362-3
  22. Gui J., Li H. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics. 2005;21(13):3001–3008. doi: 10.1093/bioinformatics/bti422
  23. Hossain S., Ahmed S.E. Penalized and Shrinkage Estimation in the Cox Proportional Hazards Model. Communications in Statistics – Theory and Methods. 2014;43(5):1026–1040. doi: 10.1080/03610926.2013.826368
  24. Hou W., Song L., Wang X. Penalized Empirical Likelihood via Bridge Estimator in Cox's Proportional Hazard Model. Communications in Statistics – Theory and Methods. 2013;43(2):426–440. doi: 10.1080/03610926.2012.657325
  25. Kauermann G. Penalized spline smoothing in multivariable survival models with varying coefficients. Computational Statistics & Data Analysis. 2005;49(1):169–186. doi: 10.1016/j.csda.2004.05.006
  26. Lin C.Y., Halabi S. A Simple Method for Deriving the Confidence Regions for the Penalized Cox's Model via the Minimand Perturbation. Commun. Stat. Theory Methods. 2017;46(10):4791–4808. doi: 10.1080/03610926.2015.1085568
  27. Park E., Ha, I.D. Penalized variable selection for accelerated failure time models. Communications for Statistical Applications and Methods. 2018;25(6):591–604. doi: 10.1002/sim.8023
  28. Shi Y., Xu D., Cao Y., Jiao Y. Variable Selection via Generalized SELO-Penalized Cox Regression Models. Journal of Systems Science and Complexity. 2019;32(2):709–736. doi: 10.1007/s11424-018-7276-8
  29. Suchting R., Hebert E.T., Ma P., Kendzor D.E., Businelle M.S. Using Elastic Net Penalized Cox Proportional Hazards Regression to Identify Predictors of Imminent Smoking Lapse. Nicotine and Tobacco Research. 2019;21(2):173–179. doi: 10.1093/ntr/ntx201
  30. Wang D., Wu T. T., Zhao Y. Penalized empirical likelihood for the sparse Cox regression model. Journal of Statistical Planning and Inference. 2019;201:71–85. doi: 10.1016/j.jspi.2018.12.001
  31. Wu T.T., Gong H., Clarke E.M. A Transcriptome Analysis by Lasso Penalized Cox Regression for Pancreatic Cancer Survival. Journal of Bioinformatics and Computational Biology. 2012;09(Supp01):63–73. doi: 10.1142/S0219720011005744
  32. Huang H.H., Liang Y. Hybrid L1/2+2 method for gene selection in the Cox proportional hazards model. Comput. Methods Programs Biomed. 2018;164:65–73. doi: 10.1016/j.cmpb.2018.06.004
  33. Huang J., Sun T., Ying Z., Yu Y., Zhang C.H. Oracle Inequalities for the Lasso in the Cox Model. Ann. Stat. 2013;41(3):1142–1165. doi: 10.1214/13-AOS1098
  34. Jiang H.K., Liang Y. The L1/2 regularization network Cox model for analysis of genomic data. Comput. Biol. Med. 2018;100:203–208. doi: 10.1016/j.compbiomed.2018.07.009
  35. Li Y., Dicker L., Zhao S.D. The Dantzig Selector for Censored Linear Regression. Models. Stat. Sin. 2014;24(1):251–2568. doi: 10.5705/ss.2011.220
  36. Liu C., Liang Y., Luan X.Z., Leung K.S., Chan T.M., Xu Z.B., Zhang H. The L1/2 regularization method for variable selection in the Cox model. Applied Soft. Computing. 2014;14:498–503. doi: 10.1016/j.asoc.2013.09.006
  37. Zhang H.H., Lu W. Adaptive Lasso for Cox’s Proportional Hazards Model. Biometrika. 2007;94(3):691–703. doi: 10.1093/biomet/asm037
  38. Bradic J., Fan J., Jiang J. Regularization for Cox’s proportional hazards model with NP-dimensionality. Annals of Statistics. 2011;39(6):3092–3120. doi: 10.1214/11-AOS911
  39. Goeman J.J. L1 penalized estimation in the Cox proportional hazards model. Biom. J. 2010;52(1):70–84. doi: 10.1002/bimj.200900028
  40. Tibshirani R. The lasso method for variable selection in the Cox model. Statistics in Medicine. 1997;16(4):385–395. doi: 10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
  41. Simon N., Friedman J., Hastie T., Tibshirani R. Regularization paths for Cox’s proportional hazards model via coordinate descent. Journal of Statistical Software. 2011;39(5):1–13. doi: 10.18637/jss.v039.i05
  42. Askarzadeh A. A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures. 2016;169:1–12. doi: 10.1016/j.compstruc.2016.03.001
  43. Kawano S. Selection of tuning parameters in bridge regression models via Bayesian information criterion. Statistical Papers. 2014;55(4):1207–1223. doi: 10.1007/s00362-013-0561-7
  44. Algamal Z.Y., Lee M.H. Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert Systems with Applications. 2015;42(23):9326–9332. doi: 10.1016/j.eswa.2015.08.016
  45. Algamal Z.Y. Shrinkage parameter selection via modified cross-validation approach for ridge regression model. Communications in Statistics-Simulation and Computation. 2020;49(7):1922–1930. doi: 10.1080/03610918.2018.1508704
  46. Algamal Z.Y. A new method for choosing the biasing parameter in ridge estimator for generalized linear model. Chemometrics and Intelligent Laboratory Systems. 2018;183:96–101. doi: 10.1016/j.chemolab.2018.10.014
  47. Rosenwald A., Wright G., Chan W.C., Connors J.M., Campo E., Fisher R.I., Gascoyne R.D., Muller-Hermelink H.K., Smeland, E.B., Giltnane, J.M., et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. New England Journal of Medicine. 2002;346(25):1937–1947. doi: 10.1056/NEJMoa012914
  48. Van Houwelingen H.C., Bruinsma T., Hart A.A., Van't Veer L.J., Wessels L.F. Cross‐validated Cox regression on microarray gene expression data. Statistics in Medicine. 2006;25(18):3201–3216. doi: 10.1002/sim.2353
  49. Beer D.G., Kardia S.L., Huang C.C., Giordano T.J., Levin A.M., Misek D.E., Lin L., Chen G., Gharib T.G., Thomas D.G., et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nature Medicine. 2002;8(8):816–824. doi: 10.1038/nm733
Содержание
Оригинальная статья
Ghada Yousif Ismail Abdallh, Zakariya Yahya Algamal An Investigational Modeling Approach for Improving Gene Selection using Regularized Cox Regression Model. Маthematical biology and bioinformatics. 2023;18(2):282-293. doi: 10.17537/2023.18.282
(опубликована на англ. яз.)

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