292
Views
0
CrossRef citations to date
0
Altmetric
Articles

Prediction models with graph kernel regularization for network data

, &
Pages 1400-1417 | Received 03 Nov 2020, Accepted 08 Jan 2022, Published online: 31 Jan 2022

References

  • E. Abbe, Community detection and stochastic block models: Recent developments, J. Mach. Learn. Res. 18 (2017), pp. 6446–6531.
  • N. Binkiewicz, J.T. Vogelstein, and K. Rohe, Covariate-assisted spectral clustering, Biometrika 104 (2017), pp. 361–377.
  • J. Chen and S. Zhang, Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data, Bioinformatics 32 (2016), pp. 1724–1732.
  • B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Ann. Stat. 32 (2004), pp. 407–499.
  • J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Am. Stat. Assoc. 96 (2001), pp. 1348–1360.
  • A. Goldenberg, A.X. Zheng, S.E. Fienberg, and E.M. Airoldi, A Survey of Statistical Network Models, Now Publishers Inc, 2010.
  • H.H. Huang and Y. Liang, Hybrid l1/2+2 method for gene selection in the cox proportional hazards model, Comput. Methods. Programs. Biomed. 164 (2018), pp. 65–73.
  • H.H. Huang and Y. Liang, An integrative analysis system of gene expression using self-paced learning and scad-net, Expert. Syst. Appl. 135 (2019), pp. 102–112.
  • H.H. Huang, X.Y. Liu, and Y. Liang, Feature selection and cancer classification via sparse logistic regression with the hybrid l1/2+2 regularization, PLoS. ONE. 11 (2016), pp. e0149675.
  • S. Kim, W. Pan, and X. Shen, Network-based penalized regression with application to genomic data, Biometrics 69 (2013), pp. 582–593.
  • C. Li and H. Li, Network-constrained regularization and variable selection for analysis of genomic data, Bioinformatics 24 (2008), pp. 1175–1182.
  • C. Li and H. Li, Variable selection and regression analysis for graph-structured covariates with an application to genomics, Ann. Appl. Stat. 4 (2010), pp. 1498.
  • T. Li, E. Levina, and J. Zhu, Prediction models for network-linked data, Ann. Appl. Stat. 13 (2019), pp. 132–164.
  • Y. Liang, C. Liu, X.Z. Luan, K.S. Leung, T.M. Chan, Z.B. Xu, and H. Zhang, Sparse logistic regression with a l 1/2 penalty for gene selection in cancer classification, BMC. Bioinformatics. 14 (2013), pp. 1–12.
  • M.E. Newman and A. Clauset, Structure and inference in annotated networks, Nat. Commun. 7 (2016), pp. 1–11.
  • W. Pan, B. Xie, and X. Shen, Incorporating predictor network in penalized regression with application to microarray data, Biometrics 66 (2010), pp. 474–484.
  • M. Pearson and P. West, Drifting smoke rings, Connections 25 (2003), pp. 59–76.
  • D.A. Spielman and S.H. Teng, Spectral sparsification of graphs, SIAM J. Comput. 40 (2011), pp. 981–1025.
  • R. Tibshirani, Regression shrinkage and selection via the lasso, J. R. Statist. Soc. Ser. B (Methodological) 58 (1996), pp. 267–288.
  • R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight, Sparsity and smoothness via the fused lasso, J. R. Statist. Soc. Ser. B (Statist. Methodol.) 67 (2005), pp. 91–108.
  • R. Wang, C. Su, X. Wang, Q. Fu, X. Gao, C. Zhang, J. Yang, X. Yang, and M. Wei, Global gene expression analysis combined with a genomics approach for the identification of signal transduction networks involved in postnatal mouse myocardial proliferation and development, Int. J. Mol. Med. 41 (2018), pp. 311–321.
  • M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, J. R. Statist. Soc. Ser. B (Statist. Methodol.) 68 (2006), pp. 49–67.
  • T. Zhang and R.K. Ando, Analysis of spectral kernel design based semi-supervised learning, in Advances in Neural Information Processing Systems, 2006, pp. 1601–1608.
  • T. Zhang, A. Popescul, and B. Dom, Linear prediction models with graph regularization for web-page categorization, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, pp. 821–826.
  • Y. Zhang, E. Levina, and J. Zhu, Community detection in networks with node features, Electron. J. Stat. 10 (2016), pp. 3153–3178.
  • H. Zou, The adaptive lasso and its oracle properties, J. Am. Stat. Assoc. 101 (2006), pp. 1418–1429.
  • H. Zou and T. Hastie, Regularization and variable selection via the elastic net, J. R. Statist. Soc. Ser. B (Statist. Methodol.) 67 (2005), pp. 301–320.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.