Using conditional inference trees and random forests to predict the bioaccumulation potential of organic chemicals

Environmental Toxicology and Chemistry

Volumn 32, Issue 5, 2013, Pages 1187-1195

  Strempel, Sebastian ^a   Nendza, Monika ^b   Scheringer, Martin ^a   Hungerbühler, Konrad ^a  

^a ETH ZURICH   (Switzerland)

^b Analytical Laboratory   (Germany)

Author keywords

Bioaccumulation; Bioconcentration factor; Conditional inference trees; Random forests; REACH

Indexed keywords

BIO-CONCENTRATION FACTORS; BIOACCUMULATION POTENTIAL; CONDITIONAL INFERENCE; DISTRIBUTION COEFFICIENT; PHYSICO-CHEMICAL DESCRIPTORS; RANDOM FORESTS; REACH; SUPERVISED MACHINE LEARNING;

BIOACCUMULATION; BIOCHEMISTRY; DECISION TREES; FORESTRY; INDUSTRIAL CHEMICALS; ORGANIC CHEMICALS; UNCERTAINTY ANALYSIS; WATER SUPPLY SYSTEMS;

ESTIMATION;

ORGANIC COMPOUND;

BIOACCUMULATION; BIOTRANSFORMATION; LEARNING; ORGANIC COMPOUND; ORGANIC POLLUTANT; PHYSICOCHEMICAL PROPERTY; PHYTOTOXICITY; SURFACE AREA;

ARTICLE; BIOACCUMULATION; BIOTRANSFORMATION; MACHINE LEARNING; NONHUMAN; PHYSICAL CHEMISTRY; PREDICTIVE VALUE; PRIORITY JOURNAL; RANDOM FOREST; SURFACE PROPERTY; TREE;

BIOTRANSFORMATION; ENVIRONMENTAL MONITORING; ENVIRONMENTAL POLLUTANTS; ENVIRONMENTAL POLLUTION; HALF-LIFE; ORGANIC CHEMICALS; TREES;

ALGORITHMS; BIOCHEMISTRY; CONCENTRATION; ENVIRONMENTAL REGULATIONS; FORESTRY; PHYSIOLOGY; WATER SUPPLY;

EID: 84876417882     PISSN: 07307268     EISSN: 15528618     Source Type: Journal    
DOI: 10.1002/etc.2150     Document Type: Article

References (38)

1. European Commission. 2006. Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) 793/93 and Commission Regulation (EC) 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. Official J Euro Union 49:1-849.
2. Dimitrov S, Dimitrova N, Parkerton T, Combers M, Bonnell M, Mekenyan O. 2005. Base-line model for identifying the bioaccummulation potential of chemicals. SAR QSAR Environ Res 16:531-554.
3. European Chemicals Agency. 2008. Guidance on information requirements and chemical safety assessment-Chapter R.7c: Endpoint specific guidance. Helsinki, Finland.
4. Nendza M, Herbst T. 2011. Screening for low aquatic bioaccumulation (2): Physico-chemical constraints. SAR QSAR Environ Res 22:351-364.
5. U.S. Environmental Protection Agency. 2012. Estimation Programs Interface Suite™ for Microsoft® Windows, Ver 4.10. Washington, DC.
6. U.S. Environmental Protection Agency. 2011. Estimation of toxicity using the Toxicity Estimation Software Tool (TEST). Washington, DC.
7. Zhao C, Boriani E, Chana A, Roncaglioni A, Benfenati E. 2008. A new hybrid system of QSAR models for predicting bioconcentration factors (BCF). Chemosphere 73:1701-1707.
8. Todeschini R, Consonni V. 2009. Molecular Descriptors for Chemoinformatics. Wiley-VCH, Weinheim, Germany.
9. Hothorn T, Hornik K, Zeileis A. 2006. Unbiased recursive partitioning: A conditional inference framework. J Comput Graph Stat 15:651-674.
10. Breiman L. 2001. Random forests. Mach Learn 45:5-32.
11. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. 2003. Random forest: A classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43:1947-1958.
12. Breiman L, Friedman JH, Olsen RA, Stone CJ. 1984. Classification and Regression Trees. Chapman & Hall/CRC Press, Boca Raton, FL, USA.
13. Wall L, Christiansen T, Orwant J. 2000. Programming Perl, 3rd ed. O'Reilly, Sebastopol, CA, USA.
14. R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
15. Fu W, Franco A, Trapp S. 2009. Methods for estimating the bioconcentration factor of ionizable organic chemicals. Environ Toxicol Chem 28:1372-1379.
16. Environment Canada. 2006. Ecological Categorization Results from the Canadian Domestic Substance List. Organisation for Economic Co-operation and Development. Ottawa, ON, Canada.
17. U.S. Environmental Protection Agency. 2011. EPI Suite Data. Washington, DC.
18. U.S. Environmental Protection Agency. 2011. Aquatic Toxicity Information Retrieval Database (AQUIRE). Duluth, MN.
19. Cefic Long-range Research Initiative. 2006. BCF Gold Standard Database. Brussels, Belgium.
20. National Institute of Technology and Evaluation. 2011. Chemical Risk Information Platform (CHRIP). Tokyo, Japan.
21. Arnot JA, Mackay D, Parkerton TF, Bonnell M. 2008. A database of fish biotransformation rates for organic chemicals. Environ Toxicol Chem 27:2263-2270.
22. Arnot JA, Mackay D, Bonnell M. 2008. Estimating metabolic biotransformation rates in fish from laboratory data. Environ Toxicol Chem 27:341-351.
23. Guha R, Howard MT, Hutchison GR, Murray-Rust P, Rzepa H, Steinbeck C, Wegner JK, Willighagen E. 2006. The blue obelisk: Interoperability in chemical informatics. J Chem Inf Model 46:991-998.
24. US Environmental Protection Agency. 2011. SPARC, Ver 4.6. Washington, DC.
25. Organisation for Economic Co-operation and Development (OECD). 1996. OECD guidelines for the testing of chemicals/section 3: Degradation and accumulation test no. 314: Simulation tests to assess the biodegradability of chemicals discharged in wastewater. Paris, France.
26. Strempel S, Scheringer M, Ng CA, Hungerbühler K. 2012. Screening for PBT chemicals among the "existing" and "new" chemicals of the EU. Environ Sci Technol 46:5680-5687.
27. Tropsha A. 2010. Best practices for QSAR model development, validation, and exploitation. Molecular Informatics 29:476-488.
28. Kohavi R, John GH. 1997. Wrappers for feature subset selection. Artif Intell 97:273-324.
29. Kursa MB, Rudnicki WR. 2010. Feature selection with the Boruta package. J Stat Softw 36:1-13.
30. Kuhn M. 2011. caret: Classification and regression training. R package, Ver 4.76. Comprehensive R Archive Network (CRAN), Wien, Austria.
31. Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F. 2012. Review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cy C 42:463-484.
32. Hastie T, Tibshirani R, Friedman J. 2001. The Elements of Statistical Learning Data Mining, Inference and Prediction. Springer, New York, NY, USA.
33. Dimitrov S, Dimitrova G, Pavlov T, Dimitrova N, Patlewicz G, Niemela J, Mekenyan O. 2005. A stepwise approach for defining the applicability domain of SAR and QSAR models. J Chem Inf Model 45:839-849.
34. Laggner C. 2005. SMARTS patterns for functional group classification. Inte:Ligand Software-Entwicklungs und Consulting GmbH, Maria Enzersdorf, Austria.
35. Nichols JW, Bonnell M, Dimitrov SD, Escher BI, Han IX, Kramer NI. 2009. Bioaccumulation assessment using predictive approaches. Integr Environ Assess Manage 4:577-597.
36. Kelder J, Grootenhuis PDJ, Bayada DM, Delbressine LPC, Ploemen JP. 1999. Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm Res 16:1514-1519.
37. Fox J. 2008. Applied Regression Analysis and Generalized Linear Models, 2nd ed. Sage Publications, Thousand Oaks, CA, USA.
38. Robinson P, MacDonald D, Davidson N, Okonski A, Sene A. 2004. Use of quantitative structure activity relationships (QSARs) in the categorization of discrete organic substances on Canada's Domestic Substance List (DSL). Environ Informat Arch 2:69-78.