Prediction of atmospheric degradation data for POPs by gene expression programming

References

• Jones , KC and de Voogt , P . 1999 . Persistent organic pollutants (POPs): state of the science . Environ. Pollut. , 100 : 209 – 221 .
   PubMed Web of Science ®Google Scholar
• Orris , P , Chary , LK , Perry , K and Asbury , J . 2000 . Persistent organic pollutants (POPs) and human health , Washington, DC : World Federation of Public Health Associations POPs Project .
   Google Scholar
• UNEP . 1998 . “ Report of the first session of the inc for an international legally binding instrument for implementing international action on certain persistent organic pollutants (POPs) ” . In UNEP Report , Vol. 15 , International Institute for Sustainable Development (IISD) .
   Google Scholar
• Webster , E , Mackay , D and Wania , F . 1998 . Evaluating environmental persistence . Environ. Toxicol. Chem. , 17 : 2148 – 2158 .
   Web of Science ®Google Scholar
• Rodan , BD , Pennington , DW , Eckley , N and Boethling , RS . 1999 . Screening for persistent organic pollutants: Techniques to provide a scientific basis for POPs in international negotiations . Environ. Sci. Technol. , 33 : 3482 – 3488 .
   Web of Science ®Google Scholar
• Mackay , D . 2001 . Multimedia Environmental Models. The Fugacity Approach , 2nd , Boca Raton, FL : Lewis Publishers .
   Google Scholar
• Gouin , T , Cousins , I and Mackay , D . 2004 . Comparison of two methods for obtaining degradation half-lives . Chemosphere , 56 : 531 – 535 .
   PubMed Web of Science ®Google Scholar
• Gramatica , P , Consonni , V and Todeschini , R . 1999 . QSAR study on the tropospheric degradation of organic compounds . Chemosphere , 38 : 1371 – 1378 .
   Web of Science ®Google Scholar
• Gramatica , P , Pozzi , S , Consonni , V and Todeschini , R . 1999 . Study of the POP atmospheric mobility by QSAR approach . Organohalogn. Compd. , 41 : 399 – 402 .
   Google Scholar
• Gramatica , P , Consolaro , F and Pozzi , S . 2001 . QSAR approach to POPs screening for atmospheric persistence . Chemosphere , 43 : 655 – 664 .
   PubMed Web of Science ®Google Scholar
• Matthies , M , Beyer , A and Mackay , D . 1999 . Long-range transport potential of PCB and PCDD/F and their classification . Organohalogn Compd. , 41 : 347 – 349 .
   Google Scholar
• Beyer , AD , Mackay , M , Matthies , F and Webster , E . 2000 . Assessing long-range transport potential of persistent organic pollutants . Environ. Sci. Technol. , 34 : 699 – 703 .
   Web of Science ®Google Scholar
• Meylan , W . 1999 . EPIWIN v.3.04 [computer program] , Syracuse, NY : Syracuse Research Corporation .
   Google Scholar
• Polanski , J and Gieleciak , R . 2003 . The comparative molecular surface analysis (CoMSA) with modified uninformative variable elimination-PLS (UVE-PLS) method: Application method: application to the steroids binding the aromatase enzyme. . J. Chem. Inf. Comput. Sci. , 43 : 656 – 66 .
   PubMedGoogle Scholar
• Burbidge , R , Trotter , M , Buxton , B and Holden , S . 2001 . “ Drug design by machine learning: Support vector machines for pharmaceutical data analysis, Computer and Chemistry Pollutants (POPs) ” . In UNEP Report , Vol. 15 , International Institute for Sustainable Development (IISD) .
   Google Scholar
• Manallack , DT and Livingstone , DJ . 1999 . Neural networks in drug discovery: Have they lived up to their promise? . Eur. J. Med. Chem. , 34 : 195 – 208 .
   Web of Science ®Google Scholar
• Cortes , C and Vapnik , V . 1995 . Support vector networks . Mach. Learn. , 20 : 273 – 293 .
   Web of Science ®Google Scholar
• Ferreira , C . 2002 . Gene Expression Programming in Problem Solving. Soft Computing and Industry-Recent Applications , 635 – 654 . Springer-Verlag, Berlin, , Germany : Applications .
   Google Scholar
• Ozlem , T and Keskin , ME . 2005 . Evaporation estimation using gene expression programming . J. App. Sci. , 5 : 508 – 512 .
   Google Scholar
• Baykasoglu , A , Dereli , T and Tanis , S . 2004 . Prediction of cement strength using soft computing techniques . Cem. Concr. Res. , 34 : 2083 – 2090 .
   Web of Science ®Google Scholar
• HyperChem 4.0, Hypercube, Inc. 1994 .
   Google Scholar
• Stewart , JPP . 1989 . MOPAC 6.0, Quantum Chemistry Program Exchange, QCPE, No. 455 , Bloomington, IN : Indiana University .
   Google Scholar
• Katritzky , AR , Lobanov , VS and Karelson , M . 1995 . CODESSA: Training Manual , Gainesville, FL : University of Florida .
   Google Scholar
• Katritzky , AR , Lobanov , VS and Karelson , M . 1994 . Comprehensive descriptors for structural and statistical analysis, Reference Manual, Version 2.0
   Google Scholar
• Ferreira , C . 2001 . Gene expression programming: A new adaptive algorithm for solving problems . Complex Syst. , 13 : 87 – 129 .
   Google Scholar
• Si , HZ , Yuan , SP , Zhang , KJ , Fu , AP , Duan , YB and Hu , ZD . 2008 . Quantitative structure activity relationship study on EC50 of anti-HIV drugs . Chem Intell. Lab. Sys. , 90 : 15 – 24 .
   Google Scholar
• Si , HZ , Zhang , KJ , Hu , ZD and Fan , BT . 2007 . QSAR model for prediction capacity factor of molecular imprinting polymer based on gene expression programming . QSAR Comb. Sci. , 26 : 41 – 50 .
   Google Scholar
• Ferreira , C . 2002 . Genetic representation and genetic neutrality in gene expression programming . Adv. Complex Syst. , 5 : 389 – 408 .
   Google Scholar
• Mitchell , M . 1996 . An Introduction to Genetic Algorithms Complex Adaptive Systems , Cambridge, MA, , USA : MIT Press .
   Google Scholar