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Articles

A robust quantitative structure–activity relationship modelling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on the rank-bridge estimator

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Pages 417-428 | Received 04 Mar 2019, Accepted 26 Apr 2019, Published online: 24 May 2019

References

  • Y. Cong, B.-K. Li, X.-G. Yang, Y. Xue, Y.-Z. Chen, and Y. Zeng, Quantitative structure–activity relationship study of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors by genetic algorithm feature selection and support vector regression, Chemom. Intel. Lab. Syst. 127 (2013), pp. 35–42. doi:10.1016/j.chemolab.2013.05.012.
  • C.U. Kim, W. Lew, M.A. Williams, H. Liu, L. Zhang, S. Swaminathan, N. Bischofberger, M.S. Chen, D.B. Mendel, C.Y. Tai, W.G. Laver, and R.C. Stevens, Influenza neuraminidase inhibitors possessing a novel hydrophobic interaction in the enzyme active site: Design, synthesis, and structural analysis of carbocyclic sialic acid analogues with potent anti-influenza activity, J. Am. Chem. Soc. 119 (1997), pp. 681–690.
  • P. Chand, Y.S. Babu, S. Bantia, S. Rowland, A. Dehghani, P.L. Kotian, T.L. Hutchison, S. Ali, W. Brouillette, Y. El-Kattan, and T.-H. Lin, Syntheses and neuraminidase inhibitory activity of multisubstituted cyclopentane amide derivatives, J. Med. Chem. 47 (2004), pp. 1919–1929. doi:10.1021/jm0303406.
  • T.-T. Dao, B.-T. Tung, P.-H. Nguyen, P.-T. Thuong, -S.-S. Yoo, E.-H. Kim, S.-K. Kim, and W.-K. Oh, C-Methylated flavonoids from Cleistocalyx operculatus and their inhibitory effects on novel influenza A (H1N1) neuraminidase, J. Nat. Prod. 73 (2010), pp. 1636–1642. doi:10.1021/np1002753.
  • U. Grienke, M. Schmidtke, J. Kirchmair, K. Pfarr, P. Wutzler, R. Durrwald, G. Wolber, K.R. Liedl, H. Stuppner, and J.M. Rollinger, Antiviral potential and molecular insight into neuraminidase inhibiting diarylheptanoids from Alpinia katsumadai, J. Med. Chem. 53 (2010), pp. 778–786. doi:10.1021/jm901440f.
  • M.K. Qasim, Z.Y. Algamal, and H.T.M. Ali, A binary QSAR model for classifying neuraminidase inhibitors of influenza A viruses (H1N1) using the combined minimum redundancy maximum relevancy criterion with the sparse support vector machine, SAR QSAR Environ. Res. 29 (2018), pp. 517–527. doi:10.1080/1062936X.2018.1491414.
  • N.A. Al-Thanoon, O.S. Qasim, and Z.Y. Algamal, A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics, Chemom. Intel. Lab. Syst. 184 (2019), pp. 142–152. doi:10.1016/j.chemolab.2018.12.003.
  • L.A. Jaeckel, Estimating regression coefficients by minimizing the dispersion of the residuals, Ann. Math. Stat. 43 (1972), pp. 1449–1458. doi:10.1214/aoms/1177692377.
  • X. Gao and J. Huang, Asymptotic analysis of high-dimensional lad regression with Lasso, Stat. Sinica. 20 (2010), pp. 1485–1506.
  • H. Wang, G. Li, and G. Jiang, Robust regression shrinkage and consistent variable selection through the LAD-Lasso, J. Busi. Econ. Stat. 25 (2007), pp. 347–355. doi:10.1198/073500106000000251.
  • O. Arslan, Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression, Comp. Stat. Data Anal. 56 (2012), pp. 1952–1965. doi:10.1016/j.csda.2011.11.022.
  • M. Norouzirad, S. Hossain, and M. Arashi, Shrinkage and penalized estimators in weighted least absolute deviations regression models, J. Stat. Comp. Simul. 88 (2018), pp. 1557–1575.
  • Q. Zheng, C. Gallagher, and K.B. Kulasekera, Robust adaptive Lasso for variable selection, Comm. Stat. - Theor. Meth. 46 (2016), pp. 4642–4659.
  • I.E. Frank and J.H. Friedman, A statistical view of some chemometrics regression tools, Tech. 35 (1993), pp. 109–135.
  • R. Tibshirani, Regression shrinkage and selection via the lasso, J. Royal Statist. Soc. Ser. B. 58 (1996), pp. 267–288.
  • J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Am. Stat. Assoc. 96 (2001), pp. 1348–1360. doi:10.1198/016214501753382273.
  • H. Zou and T. Hastie, Regularization and variable selection via the elastic net, J. Royal. Stat. Soc. Ser. B. (Stat. Meth.) 67 (2005), pp. 301–320. doi:10.1111/j.1467-9868.2005.00503.x.
  • H. Zou, The adaptive lasso and its oracle properties, J. Am. Stat. Assoc. 101 (2006), pp. 1418–1429. doi:10.1198/016214506000000735.
  • A. Turkmen and O. Ozturk, Rank-based ridge estimation in multiple linear regression, J. Nonp. Stat. 26 (2014), pp. 737–754. doi:10.1080/10485252.2014.964714.
  • C. Leng, Variable selection and coefficient estimation via regularized rank regression, Stat. Sini. 20 (2010), pp. 167–181.
  • H.-J. Kim, E. Ollila, and V. Koivunen, New robust LASSO method based on ranks, 23rd Euro. Sign. Proc. Conf .(EUSIPCO) 24 (2015), pp. 673–682.
  • B.A. Johnson and L. Peng, Rank-based variable selection, J. Nonp. Stat. 20 (2008), pp. 241–252. doi:10.1080/10485250801998950.
  • L. Wang andR. Li, Weighted Wilcoxon-type smoothly clipped absolute deviation method, Biometrics 65 (2009), pp. 564–571. doi:10.1111/j.1541-0420.2008.01099.x.
  • H. Yang, C. Guo, and J. Lv, SCAD penalized rank regression with a diverging number of parameters, J. Mult. Anal. 133 (2015), pp. 321–333. doi:10.1016/j.jmva.2014.09.014.
  • J.-T. Park and K.-M. Jung, Penalized rank regression estimator with the smoothly clipped absolute deviation function, Comm. Stat. Appl. Meth. 24 (2017), pp. 673–683.
  • A.L. Liu, H.D. Wang, S.M. Lee, Y.T. Wang, and G.H. Du, Structure-activity relationship of flavonoids as influenza virus neuraminidase inhibitors and their in vitro anti-viral activities, Bioorg. Med. Chem. 16 (2008), pp. 7141–7147. doi:10.1016/j.bmc.2008.06.049.
  • R. Todeschini, V. Consonni, A. Mauri, and M. Pavan, Dragon 6, 2012.
  • J. Fan and L. Runze, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Am. Stat. Assoc. 96 (2001), pp. 1348–1360. doi:10.1198/016214501753382273.
  • I.E. Frank and J.H. Friedman, A statistical view of some chemometrics regression tools, Technometrics 35 (1993), pp. 109–135. doi:10.1080/00401706.1993.10485033.
  • C. Park and Y.J. Yoon, Bridge regression- Adaptivity and group selection, J. Stat. Plan. Infer. 141 (2011), pp. 3506–3519. doi:10.1016/j.jspi.2011.05.004.
  • W.J. Fu, Penalized regressions: The bridge versus the lasso, J. Comp. Graph. Stat. 7 (1998), pp. 397–416.
  • K. Knight and W. Fu, Asymptotics for lasso-type estimators, Ann. Stat. 28 (2000), pp. 1356–1378. doi:10.1214/aos/1015957397.
  • J. Huang, J.L. Horowitz, and S. Ma, Asymptotic properties of bridge estimators in sparse high-dimensional regression models, Ann. Stat. 36 (2008), pp. 587–613. doi:10.1214/009053607000000875.
  • J. Huang, S. Ma, H. Xie, and C.-H. Zhang, A group bridge approach for variable selection, Biometrika 96 (2009), pp. 339–355. doi:10.1093/biomet/asp020.
  • M. Wang, L. Song, and X. Wang, Bridge estimation for generalized linear models with a diverging number of parameters, Stat. Prob. Lett. 80 (2010), pp. 1584–1596. doi:10.1016/j.spl.2010.06.012.
  • H. Zou andR. Li, One-step sparse estimates in nonconcave penalized likelihood models, Ann. Stat. 36 (2008), pp. 1509–1533. doi:10.1214/009053607000000802.
  • H. Wang, R. Li, and C.-L. Tsai, Tuning parameter selectors for the smoothly clipped absolute deviation method, Biom. Trus. 94 (2007), pp. 553–568. doi:10.1093/biomet/asm053.
  • J. Fan and J. Lv, Sure independence screening for ultra-high-dimensional feature space, J. Royal Stat. Soc. Ser. B. (Stat. Method.) 70 (2008), pp. 849–911. doi:10.1111/j.1467-9868.2008.00674.x.
  • Z.Y. Algamal, M.H. Lee, and A.M. Al-Fakih, High-dimensional quantitative structure-activity relationship modeling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on a two-stage adaptive penalized rank regression, J. Chemom. 30 (2016), pp. 50–57. doi:10.1002/cem.2766.
  • Y. Li, Y. Kong, M. Zhang, A. Yan, and Z. Liu, Using support vector machine (SVM) for classification of selectivity of H1N1 neuraminidase inhibitors, Mol. Inform. 35 (2016), pp. 116–124. doi:10.1002/minf.201500107.
  • Z.Y. Algamal, M.H. Lee, and A.M. Al-Fakih, High-dimensional quantitative structure–activity relationship modeling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on a two-stage adaptive penalized rank regression, J. Chemom. 30 (2016), pp. 50–57. doi:10.1002/cem.2766.

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