Classification of bioaccumulative and non-bioaccumulative chemicals using statistical learning approaches

Molecular Diversity

Volumn 12, Issue 3-4, 2008, Pages 157-169

  Sun, Xiuli ^a   Li, Yan ^b   Liu, Xianjie ^a   Ding, Jun ^a   Wang, Yonghua ^a   Shen, Hui ^a   Chang, Yaqing ^a  

^a DALIAN OCEAN UNIVERSITY   (China)

^b DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Bioconcentration; In silico prediction; Quantitative structure activity relationships (QSAR); Statistical methods

Indexed keywords

CHEMICAL COMPOUND;

ACCURACY; ARTICLE; BIOACCUMULATION; DRUG ACTIVITY; FISH; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RANDOM FOREST; SUPPORT VECTOR MACHINE; ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; CHEMISTRY; CLASSIFICATION; DANGEROUS GOODS; METABOLISM; WATER POLLUTANT;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; FISHES; HAZARDOUS SUBSTANCES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; WATER POLLUTANTS, CHEMICAL;

EID: 60549108789     PISSN: 13811991     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11030-008-9092-x     Document Type: Article

References (42)

1. O Faroon D Jones C De Rosa 2000 Effects of polychlorinated biphenyls on the nervous system Toxicol Ind Health 16 305 333 10.1177/074823370001600708
2. JA Arnot FAPC Gobas 2006 A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms Environ Rev 14 257 297 10.1139/A06-005
3. UNEP (1998) Report for the first session of the INC for an international legally binding instrument for implementing international action on certain persistent organic pollutants (POPs). UNEP Report, International Institute for Sustainable Development (IISD) 15(10) or http://irptc.unep.ch/pops/
4. ow/logBCF correlations. In: Nagel R, Loskill R (eds) Bioaccumulation in aquatic systems, VCH, Weinheim, pp 43-66
5. D Mackay 1982 Correlation of bioconcentration factors Environ Sci Technol 16 274 278 10.1021/es00099a008
6. P Isnard S Lambert 1988 Estimating bioconcentration factors from octanol-water partition coefficient and aqueous solubility Chemosphere 17 21 34
7. Government of Canada (1995) Toxic substances management policy persistence and bioaccumulation criteria. Ottawa Canada No En 40-499:21
8. GD Veith DL DeFoe BV Bergstedt 1979 Measuring and estimating the bioconcentration factor of chemicals in fish J Fish Res Board Can 36 1040 1048
9. WM Meylan PH Howard RS Boethling D Aronson H Printup S Gouchie 1999 Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partitioning coefficient Environ Toxicol Chem 18 664 672 10.1897/1551-5028(1999)018<0664:IMFEBB>2.3.CO;2
10. SD Dimitrov OG Mekenyan JD Walker 2002 Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals SAR QSAR Environ Res 13 177 184 10.1080/10629360290002299
11. Environment Canada (1994) Criteria for the selection of substances for virtual elimination. Final Report of the ad hoc Science Group on Criteria. A companion document to 'Towards a Toxic Substances Management Policy for Canada'. Ottawa, Ontario 25 12
12. State of Knowledge Report of the UNECE Task force on Pesestence Organic Pollutants (1994)
13. Priority Setting for Long-range Transboundary Air Pollution by Persistent Organic Chemicals (1993) AEA Technology Report
14. Tyle H, Larsen HS, Wedebye L, Sijm D, Krog T, Niemelä J (2002) Identification of potential PBTs and vPvBs by use of QSARs. Danish EPA Copenhagen
15. SD Dimitrov NC Dimitrova JD Walker GD Veith OG Mekenyan 2003 Bioconcentration potential predictions based on molecular atributes. An early worning system for chemicals found in humans, fish and wildlife QSAR Comb Sci 22 58 68
16. S Dimitrov N Dimitrova T Parkerton M Comber M Bonnell O Mekenyan 2005 Base-line model for identifying the bioaccumulation potential of chemicals SAR QSAR Environ Res 16 531 544
17. X Lu S Tao H Hu RW Dawson 2000 Estimation of bioconcentration factors of nonionic organic compounds in fish by molecular connectivity indices and polarity correction factors Chemosphere 41 1675 1688
18. A Nordberg C Rudén 2007 The usefulness of the bioconcentration factor as a tool for priority setting in chemicals control Toxic Lett 168 113 120
19. LH Hall BK Mohney LB Kier 1991 The electrotopological state: an atom index for QSAR Quant Struct Act Relat 10 43 58
20. Hansch C, Leo A (1979) A substituent constants for correlation analysis in chemistry and biology. John Wiley & Sons, New York
21. Kohonen T (1995) Self-organizing maps, vol 30. Springer, Berlin/Heidelberg
22. Ramsden CA, InHansch C (1990) Comprehensive medicinal chemistry, vol 4. Pergamon Press Oxford, NewYork
23. Y Wang Y Li B Wang 2007 An in silico method for screening nicotine derivatives as cytochrome P450 2A6 selective inhibitors based on Kernel partial least squares Int J Mol Sci 8 166 179
24. R Leardi L Nørgaard 2000 Application of genetic algorithm-PLS for feature selection in spectral data sets J Chem Intel Lab Syst 14 643 655
25. DV Nguyen DM Rocke 2002 Tumor classification by partial least squares using microarray gene expression data Bioinformatics 18 39 50
26. Wang HW (2000) Partial least-squares regression-method and applications. National Defense Industrial Press, pp 236-270
27. Eriksson L, Johansson E, Kettaneh-Wold N, Wold S (eds) (2001) Multi- and megavariate data analysis principles and applications. Umetries Academy, pp 123-131
28. K Hasegawa K Funatsu 2000 Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships SAR QSAR Environ Res 11 189 209
29. IE Frank JH Friedman 1993 A statistical view of some chemometric regression tools (with discussion) Technometrics 35 109 147
30. Quinlan JR (1993) C4.5: Programs for machine learning (morgan kaufmann series in machine learning). Morgan Kaufmann Publishers Inc, San Mateo, CA
31. H Li CY Ung CW Yap Y Xue ZR Li YZ Chen 2006 Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods J Mol Graph Model 25 313 323
32. Vapnik VN (1998) Statistical learning theory (adaptive and learning systems for signal processing, communications and control series). John Wiley & Sons, New York. A Wiley-Interscience Publication
33. Cristianini N, Shawe-Taylor J (eds) (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press
34. CJC Burges 1998 A tutorial on support vector machine for pattern recognition Data Min Knowl Disc 2 121 167
35. R Burbidge M Trotter B Buxton S Holden 2001 Drug design by machine learning: support vector machines for pharmaceutical data analysis Comput Chem 26 5 14
36. M Song CM Breneman J Bi N Sukumar KP Bennett S Cramer N Tugcu 2002 Prediction of protein retention times in anion-exchange chromatography systems using support vector regression J Chem Inf Comput Sci 42 1347 1357
37. S Kramer E Frank C Helma 2002 Fragment generation and support vector machines for inducing SARs SAR QSAR Environ Res 13 509 523
38. XJ Yao A Panaye JP Doucet HF Chen BT Fan 2005 Comparative classification study of toxicity mechanisms using support vector machines and radial basis function neural networks Anal Chim Acta 535 259 273
39. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the 5th annual workshop on computational learning theory, ACM Press, Pittsburgh PA, pp 144-152
40. L Breiman 2001 Random forests J Mach Learn Res 45 5 32
41. V Svetnik A Liaw C Tong JC Culberson RP Sheridan BP Feuston 2003 Random forest: a classification and regression tool for compound classification and QSAR modeling J Chem Inf Comput Sci 43 1947 1958
42. W Baolin A Tom F David M Walter M Gil 2003 Comparison of statistical methods for classication of ovarian cancer using mass spectrometry data Bioinformatics 19 1636 1643