330 related articles for article (PubMed ID: 16918241)
1. Classification of a diverse set of Tetrahymena pyriformis toxicity chemical compounds from molecular descriptors by statistical learning methods.
Xue Y; Li H; Ung CY; Yap CW; Chen YZ
Chem Res Toxicol; 2006 Aug; 19(8):1030-9. PubMed ID: 16918241
[TBL] [Abstract][Full Text] [Related]
2. Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods.
Li H; Ung CY; Yap CW; Xue Y; Li ZR; Chen YZ
J Mol Graph Model; 2006 Nov; 25(3):313-23. PubMed ID: 16497524
[TBL] [Abstract][Full Text] [Related]
3. In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.
Cheng F; Shen J; Yu Y; Li W; Liu G; Lee PW; Tang Y
Chemosphere; 2011 Mar; 82(11):1636-43. PubMed ID: 21145574
[TBL] [Abstract][Full Text] [Related]
4. Prediction of factor Xa inhibitors by machine learning methods.
Lin HH; Han LY; Yap CW; Xue Y; Liu XH; Zhu F; Chen YZ
J Mol Graph Model; 2007 Sep; 26(2):505-18. PubMed ID: 17418603
[TBL] [Abstract][Full Text] [Related]
5. QSTR with extended topochemical atom (ETA) indices. 12. QSAR for the toxicity of diverse aromatic compounds to Tetrahymena pyriformis using chemometric tools.
Roy K; Ghosh G
Chemosphere; 2009 Nov; 77(7):999-1009. PubMed ID: 19709717
[TBL] [Abstract][Full Text] [Related]
6. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.
Zhu H; Tropsha A; Fourches D; Varnek A; Papa E; Gramatica P; Oberg T; Dao P; Cherkasov A; Tetko IV
J Chem Inf Model; 2008 Apr; 48(4):766-84. PubMed ID: 18311912
[TBL] [Abstract][Full Text] [Related]
7. Prediction of antibacterial compounds by machine learning approaches.
Yang XG; Chen D; Wang M; Xue Y; Chen YZ
J Comput Chem; 2009 Jun; 30(8):1202-11. PubMed ID: 18988254
[TBL] [Abstract][Full Text] [Related]
8. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection.
Tetko IV; Sushko I; Pandey AK; Zhu H; Tropsha A; Papa E; Oberg T; Todeschini R; Fourches D; Varnek A
J Chem Inf Model; 2008 Sep; 48(9):1733-46. PubMed ID: 18729318
[TBL] [Abstract][Full Text] [Related]
9. Prediction of genotoxicity of chemical compounds by statistical learning methods.
Li H; Ung CY; Yap CW; Xue Y; Li ZR; Cao ZW; Chen YZ
Chem Res Toxicol; 2005 Jun; 18(6):1071-80. PubMed ID: 15962942
[TBL] [Abstract][Full Text] [Related]
10. Modeling the toxicity of chemicals to Tetrahymena pyriformis using heuristic multilinear regression and heuristic back-propagation neural networks.
Kahn I; Sild S; Maran U
J Chem Inf Model; 2007; 47(6):2271-9. PubMed ID: 17985864
[TBL] [Abstract][Full Text] [Related]
11. Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods.
Lv W; Xue Y
Eur J Med Chem; 2010 Mar; 45(3):1167-72. PubMed ID: 20053484
[TBL] [Abstract][Full Text] [Related]
12. QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis.
Kar S; Harding AP; Roy K; Popelier PL
SAR QSAR Environ Res; 2010 Jan; 21(1):149-68. PubMed ID: 20373218
[TBL] [Abstract][Full Text] [Related]
13. Application of random forest approach to QSAR prediction of aquatic toxicity.
Polishchuk PG; Muratov EN; Artemenko AG; Kolumbin OG; Muratov NN; Kuz'min VE
J Chem Inf Model; 2009 Nov; 49(11):2481-8. PubMed ID: 19860412
[TBL] [Abstract][Full Text] [Related]
14. Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods.
Yap CW; Li ZR; Chen YZ
J Mol Graph Model; 2006 Mar; 24(5):383-95. PubMed ID: 16290201
[TBL] [Abstract][Full Text] [Related]
15. A novel approach to predict a toxicological property of aromatic compounds in the Tetrahymena pyriformis.
González MP; Díaz HG; Cabrera MA; Ruiz RM
Bioorg Med Chem; 2004 Feb; 12(4):735-44. PubMed ID: 14759733
[TBL] [Abstract][Full Text] [Related]
16. Selection of data sets for QSARs: analyses of Tetrahymena toxicity from aromatic compounds.
Schultz TW; Netzeva TI; Cronin MT
SAR QSAR Environ Res; 2003 Feb; 14(1):59-81. PubMed ID: 12688416
[TBL] [Abstract][Full Text] [Related]
17. 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 pyriformis.
Panaye A; Fan BT; Doucet JP; Yao XJ; Zhang RS; Liu MC; Hu ZD
SAR QSAR Environ Res; 2006 Feb; 17(1):75-91. PubMed ID: 16513553
[TBL] [Abstract][Full Text] [Related]
18. Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods.
Li H; Yap CW; Ung CY; Xue Y; Cao ZW; Chen YZ
J Chem Inf Model; 2005; 45(5):1376-84. PubMed ID: 16180914
[TBL] [Abstract][Full Text] [Related]
19. Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression.
Yao XJ; Panaye A; Doucet JP; Zhang RS; Chen HF; Liu MC; Hu ZD; Fan BT
J Chem Inf Comput Sci; 2004; 44(4):1257-66. PubMed ID: 15272833
[TBL] [Abstract][Full Text] [Related]
20. In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches.
Singh KP; Gupta S
Toxicol Appl Pharmacol; 2014 Mar; 275(3):198-212. PubMed ID: 24463095
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]