Quantitative structure-toxicity relationships (QSTRs): A comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformisFootnote§

§ Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).

References

• Cronin , MTD . 2000 . Curr. Opin. Drug Discov. Dev. , 3 : 292
   PubMedGoogle Scholar
• Schultz , TW and Cronin , MTD . 2003 . Environ. Toxicol. Chem. , 22 : 599
   PubMed Web of Science ®Google Scholar
• Schultz , TW , Cronin , MTD and Nevetza , TI . 2003 . J. Mol. Struct.-Theochem. , 622 : 23
   Web of Science ®Google Scholar
• Ren , S . 2003 . J. Chem. Inf. Comput. Sci. , 43 : 1679
   PubMedGoogle Scholar
• Mekenyan , OG and Veith , GD . 1993 . SAR QSAR Environ. Res. , 1 : 335
   PubMedGoogle Scholar
• Cronin , MTD , Manga , N , Seward , JR , Sinks , GD and Schultz , TW . 2001 . Chem. Res. Toxicol. , 14 : 1498
   PubMed Web of Science ®Google Scholar
• Schultz , TW , Netvzeva , TI and Cronin , MTD . 2003 . SAR QSAR Environ. Res. , 14 : 59
   PubMed Web of Science ®Google Scholar
• Cronin , MTD , Aptula , AO , Duffy , JC , Netveza , TI , Rowe , PH , Valkova , IV and Schultz , TW . 2002 . Chemosphere , 49 : 1201
   PubMed Web of Science ®Google Scholar
• Cronin , MTD and Schultz , TW . 2003 . J. Mol. Struct.-Theochem , 622 : 39
   Web of Science ®Google Scholar
• McFarland , JW . 1970 . J. Med. Chem. , 13 : 1092
   PubMed Web of Science ®Google Scholar
• Kaiser , KLE . 2003 . Comb. Sci. , 22 : 185
   Google Scholar
• Kaiser , KLE . 2003 . J. Mol. Struct.-Theochem. , 622 : 85
   Web of Science ®Google Scholar
• Walzack , B and Massart , DL . 2000 . Chemom. Intell. Lab. Syst. , 50 : 179
   Web of Science ®Google Scholar
• Yao , XY , Panaye , A , Doucet , JP , Zhang , RS , Chen , HF , Liu , MC , Hu , ZD and Fan , BT . 2004 . J. Chem. Inf. Comput. Sci. , 44 : 1257
   PubMedGoogle Scholar
• Yao , XJ , Fan , BT , Doucet , JP , Panaye , A , Liu , MC , Zhang , RS , Zhang , XY and Hu , ZD . 2003 . QSAR Combi. Sci. , 22 : 29
   Google Scholar
• Yao , XJ . 2004 . “ Méthodes non-linéaires (ANNs, SVMs): Applications à la classification et à la corrélation des propriétés physicochimiques et biologiques ” . In Ph.D. Thesis , 7 University Paris .
   Google Scholar
• Vapnik , VN . 1995 . The Nature of Statistical Learning Theory , Berlin : Springer . and Statistical Learning Theory, J. Wiley, New York (1998)
   Google Scholar
• Christianini , N and Shawe-Taylor , J . 2000 . An Introduction to Support Vector Machines and Other Kernel-based Functions Learning Methods , UK : Cambridge University Press .
   Google Scholar
• Furey , TS , Cristianini , N , Duffy , N , Bednarski , DW , Schummer , M and Haussler , D . 2000 . Bioinformatics , 16 : 906
   PubMed Web of Science ®Google Scholar
• Liu , HX , Zhang , RS , Yao , XJ , Liu , MC , Hu , ZD and Fan , BT . 2003 . J. Chem. Inf. Comp. Sci. , 43 : 900
   PubMedGoogle Scholar
• Chang , RF , Wu , WJ , Moon , WK , Chou , YH and Chen , DR . 2003 . Acad. Radiol. , 43 : 900
   Google Scholar
• Amendolia , SR , Cossu , R , Ganadu , ML , Golosio , B , Masala , GL and Mura , GM . 2003 . Chemom. Intel. Lab. Syst. , 69 : 13
   Web of Science ®Google Scholar
• Cai , YD , Liu , XJ , Xu , XB and Chou , KC . 2002 . Comput. Chem. , 26 : 293
   PubMedGoogle Scholar
• Cai , YD , Liu , XJ , Xu , XB and Chou , KC . 2002 . J. Comput. Chem. , 23 : 267
   PubMed Web of Science ®Google Scholar
• Xue , Y , Yap , CW , Sun , LZ , Cao , ZW , Wang , JF and Chen , YZ . 2004 . J. Chem. Inf. Comput. Sci. , 44 : 1497
   PubMedGoogle Scholar
• Zernov , VV , Balakin , KV , Ivaschzenko , AA , Savchuk , NP and Pletnev , LV . 2003 . J. Chem. Inf. Comput. Sci. , 43 : 2048
   PubMedGoogle Scholar
• Czerminski , R , Yasri , A and Hartsough , D . 2001 . Quant. Struct-Act. Relat. , 20 : 227
   Google Scholar
• Burbidge , R , Trotter , M , Buxton , B and Holden , S . 2001 . Comput. Chem. , 26 : 5
   PubMedGoogle Scholar
• Warmuth , MK , Liao , J , Ratsch , G , Mathieson , M , Putta , S and Lemmen , C . 2003 . J. Chem. Inf. Comput. Sci. , 43 : 667
   PubMedGoogle Scholar
• Kramer , S , Franck , E and Helma , C . 2002 . SAR QSAR Environ. Res. , 13 : 509
   PubMed Web of Science ®Google Scholar
• Distante , C , Ancona , N and Siciliano , P . 2003 . Sensors and Actuators B , 88 : 30
   Web of Science ®Google Scholar
• Cao , LJ . 2003 . Neurocomputing , 51 : 321
   Web of Science ®Google Scholar
• Song , M , Breneman , CM , Bi , J , Sukumar , N , Bennett , KP , Cramer , S and Tugcu , N . 2002 . J. Chem. Inf. Comput. Sci. , 42 : 1247
   PubMedGoogle Scholar
• Tugcu , N , Song , M , Breneman , CM , Sukumar , N , Bennett , KP and Cramer , SM . 2003 . Anal. Chem. , 75 : 3563
   PubMed Web of Science ®Google Scholar
• Liu , HX , Zhang , RS , Yao , XJ , Liu , MC , Hu , ZD and Fan , BT . 2003 . J. Chem. Inf. Comput. Sci. , 43 : 1288
   PubMedGoogle Scholar
• Xue , CX , Zhang , RS , Liu , HX , Liu , MC , Hu , ZD and Fan , BT . 2004 . J. Chem. Inf. Comput. Sci. , 44 : 1267
   PubMedGoogle Scholar
• Liu , HX , Zhang , RS , Yao , XJ , Liu , MC , Hu , ZD and Fan , BT . 2005 . J. Comput.-Aided Mol. Des. , 18 : 339
   Google Scholar
• Liu , HX , Hu , RJ , Zhang , RS , Yao , XJ , Liu , MC , Hu , ZD and Fan , BT . 2005 . J. Comput.-Aided Mol. Des. , 19 : 33
   PubMed Web of Science ®Google Scholar
• Liu , HX , Yao , XJ , Xue , CX , Zhang , RS , Liu , MC , Hu , ZD and Fan , BT . 2005 . Anal. Chim. Acta , 542 : 249
   Web of Science ®Google Scholar
• Yao , XJ , Liu , HX , Zhang , RS , Liu , MC , Hu , ZD , Panaye , A , Doucet , JP and Fan , BT . 2006 . Mol. Pharm. , 2 : 348
   Google Scholar
• Hastic , TJ and Tibshirani , RJ . 1990 . Generalized Additive Models , New York : Chapman and Hall .
   Google Scholar
• Osborne , MR , Presnell , B and Turlach , BA . 2000 . J. Comput. Graphical Stat. , 9 : 319
   Web of Science ®Google Scholar
• Cleveland , WS . 1979 . J. Amer. Stat. Asso. , 46 : 175
   Google Scholar
• Friedman , JH . 1991 . Annals Stat. , 19 : 1
   Web of Science ®Google Scholar
• Friedman , JH and Stuetzle , W . 1981 . J. Amer. Stat. Asso. , 76 : 817
   Web of Science ®Google Scholar
• Müller , B . 1990 . Neural Networks. An introduction , New York : Springer Verlag .
   Google Scholar
• Orr , MJL . 1996 . Introduction to Radial Basis Function Networks , Centre for Cognitive Science, Edinburgh University .
   Google Scholar
• Orr , MJL . 1996 . MATLAB Routines for Subset Selection and Ridge Regression in Linear Neural Networks , Centre for Cognitive Science, Edinburgh University .
   Google Scholar
• Niwa , T . 2003 . J. Chem. Inf. Comput. Sci. , 43 : 113
   PubMedGoogle Scholar
• Parzen , E . 1962 . Ann. Math. Stat. , 3 : 1065
   Google Scholar
• Schölkopf , B and Smola , A . 2002 . Learning with Kernels , Cambridge, MA : MIT Press .
   Google Scholar
• Chang , CC and Lin , CJ . LIBSVM. A Library for Support Vector Machines, version 2-31 Available online at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/. The following expressions are used in LIBSVM for the available kernels: Radial basis: exp(−g*|u − v|2); Linear: u′*v; Polynomial: (g*u’*v + r) d ; Sigmoidal: tanh (g*u′*v + r)
   Google Scholar
• Schultz , TW . 1997 . Toxicol. Methods , 7 : 289
   Google Scholar
• To get an idea of the order of magnitude of the variations induced on log [1/IGC50] by changes in Amax or LogKow, one can refer to the MLR established in [6] A change of 0.1 unit in logKow or 0.01 for Amax increases toxicity by about 0.04 and 0.1unit, respectively
   Google Scholar