The identification of complex interactions in epidemiology and toxicology: A simulation study of boosted regression trees

Environmental Health: A Global Access Science Source

Volumn 13, Issue 1, 2014, Pages

  Lampa, Erik ^a   Lind, Lars ^a   Lind, P Monica ^a   Bornefalk Hermansson, Anna ^b  

^a UPPSALA UNIVERSITY   (Sweden)

^b UPPSALA UNIVERSITY HOSPITAL   (Sweden)

Author keywords

Boosting; Epidemiology; Interactions; Toxicology

Indexed keywords

DATA SET; EPIDEMIOLOGY; POLLUTANT; REAL TIME; REGRESSION ANALYSIS; TOXICOLOGY;

ANALYTIC METHOD; ARTICLE; BOOSTED REGRESSION TREE; ENVIRONMENTAL FACTOR; EPIDEMIOLOGICAL DATA; SIMULATION; STOCHASTIC MODEL; TOXICOLOGY; COMPUTER SIMULATION; ENVIRONMENTAL MONITORING; HUMAN; POLLUTANT; PROCEDURES; REGRESSION ANALYSIS; STATISTICAL MODEL;

POLLUTANT;

COMPUTER SIMULATION; ENVIRONMENTAL MONITORING; ENVIRONMENTAL POLLUTANTS; HUMANS; MODELS, STATISTICAL; REGRESSION ANALYSIS;

EID: 84905384555     PISSN: None     EISSN: 1476069X     Source Type: Journal    
DOI: 10.1186/1476-069X-13-57     Document Type: Article

References (52)

1. The search for synergy: a critical review from a Response surface perspective. Greco WR, Bravo G, Parsons JC, Pharmacol Rev 1995 47 331 385 7568331
2. Contrasting theories in epidemiology and toxicology. Howard GJ, Webster TF, Environ Health Persp 2013 121 1 6
3. Toxicity from combined exposure to chemicals. Kortenkamp A, Altenburger R, Mixture Toxicity. Linking Approaches from Ecological and Human Toxicology Pensacola, FL: SETAC Press, van Gestel CAM, Jonker MJ, Kammenga JE, Laskowski R, Svendsen C, 2011 95 119
4. A unifying concept for assessing toxicological interactions: changes in slope. Gennings C, Carter WH, Carchman RA, Teuschler LK, Simmons JE, Carney EW, Tox Sci 2005 88 287 297 10.1093/toxsci/kfi275
5. Estrogenic activity of {UV} filter mixtures. Kunz P, Fent K, Toxicol Appl Pharm 2006 217 86 99 10.1016/j.taap.2006.07.014
6. Mixtures of endocrine disrupting contaminants modelled on human high end exposures: an exploratory study in rats. Christiansen S, Kortenkamp A, Axelstad M, Boberg J, Scholze M, Jacobsen PR, Faust M, Lichtensteiger W, Schlumpf M, Burdorf A, Hass U, Int J Androl 2012 35 303 316 10.1111/j.1365-2605.2011.01242.x 22372636
7. Combined toxicity of pesticide mixtures on green algae and photobacteria. Liu S, Wang C, Zhang J, Zhu X, WY L, Ecotox Environ Safe 2013 95 98 103
8. Individual and combined effects of mycotoxins from typical indoor moulds. Mueller A, Schlink U, Wichmann G, Bauer M, Graebsch C, Schüürmann G, Herbarth O, Toxicol in Vitro 2013 27 1970 1978 10.1016/j.tiv.2013.06.019 23851004
9. Testing for departures from additivity in mixtures of perfluoroalkyl acids (PFAAs). Carr CK, Watkins AM, Wolf CJ, Abbott BD, Lau C, Gennings C, Toxicology 2013 306 169 175 23470359
10. Associations of early childhood manganese and lead coexposure with neurodevelopment. Claus Henn B, Schnaas L, Ettinger AS, Schwartz J, Lamadrid-Figueroa H, Hernández-Avila M, Amarasiriwardena C, Hu H, Bellinger DC, Wright RO, Environ Health Persp 2012 120 126 136
11. Association of tobacco and lead exposures with attention-deficit/hyperactivity disorder. Froelich TE, Lanphear BP, Auinger P, Hornung R, Epstein JR, Braun J, Kahn RS, Pediatrics 2009 124 1054 1063 10.1542/peds.2009-0738
12. Estimating the health effects of exposure to multi-pollutant mixture. Billionnet C, Sherrill D, Annesi-Maesano I, Ann Epidemiol 2012 22 126 141 10.1016/j.annepidem.2011.11.004 22226033
13. An environment-wide association study (EWAS) on type 2 diabetes mellitus. Patel CJ, Bhattacharya J, Butte AJ, PLoS ONE 2010 5 10746 10.1371/journal.pone.0010746
14. Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease. Patel CJ, Chen R, Butte AJ, Bioinformatics 2012 28 121 126 10.1093/bioinformatics/btr605 22057161
15. Systematic identification of interaction effects between genom- and environment-wide associations in type 2 diabetes mellitus. Patel CJ, Chen R, Kodama K, Ioannis JPA, Butte AJ, Hum Genet 2013 132 495 598 10.1007/s00439-012-1258-z 23334806
16. In silico toxicity prediction by support vector machine and SMILES representation-based string kernel. Cao DS, Zhao JC, Yang YN, Zhao CX, Yan J, Liu S, Hu QN, Xu QS, Liang YZ, SAR QSAR Environ Res 2012 23 141 153 10.1080/1062936X.2011.645874 22224501
17. Support vector machine: classifying and predicting mutagenicity of complex mixtures based on pollution profiles. Zheng W, Tian D, Wang X, Tian W, Zhang H, Jiang S, He G, Zheng Y, Qu W, Toxicology 2013 313 151 159 10.1016/j.tox.2013.01.016 23395826
18. Predicting chemical ocular toxicity using a combinatorial QSAR approach. Solimeo R, Zhang J, Kim M, Sedykh A, Zhu H, Chem Res Toxicol 2012 25 2763 2769 10.1021/tx300393v 23148656
19. Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches. Singh KP, Gupta S, Rai P, Ecotox Environ Safe 2013 95 221 233
20. Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods. Zang Q, Rotroff DM, Judson RF, J Chem Inf Model 2013 53 3244 3261 10.1021/ci400527b 24279462
21. Association between serum concentrations of persistent organic pollutants and γ glutamyltransferase: results from the national health and examination survey 1999-2002. Lee DH, Jacobs Jr DR, Clin Chem 2006 52 1825 1827 10.1373/clinchem.2006.071563 16940464
22. Breiman L, Friedman J, Stone CJ, Olshen RA, Classification and Regression Trees Boca Raton, FL: Chapman & Hall 1984
23. Hastie T, Tibshirani R, Friedman J, The Elements of Statistical Learning. Data Mining, Inference, and Prediction New York, NY: Springer 2009
24. Harrell Jr FE, Regression Modeling Strategies. With Applications to Linear Models, Logistic Regression and Survival Analysis New York, NY: Springer 2001
25. Ripley BD, Venables WN, Modern Applied Statistics with S New York, NY: Springer 2002
26. Stochastic gradient boosting. Friedman JH, Comput Stat Data An 2002 38 367 378 10.1016/S0167-9473(01)00065-2
27. Ridgeway G, With contributions from others: gbm: Generalized Boosted Regression Models 2013 R package version 2.1 [ http://CRAN.R-project.org/package=gbm ]
28. Additive logistic regression: a statistical view of boosting. Friedman J, Hastie T, Tibshirani R, Ann Stat 2000 28 337 407
29. Greedy function approximation: a gradient boosting machine. Friedman JH, Ann Stat 2001 29 1189 1232
30. A working guide to boosted regression trees. Elith J, Leathwick JR, Hastie T, J Anim Ecol 2008 77 802 813 10.1111/j.1365-2656.2008.01390.x 18397250
31. Predictive learning via rule esembles. Friedman JH, Popescu BE, Ann Appl Stat 2008 2 916 954 10.1214/07-AOAS148
32. Wood SN, Generalized Additive Models. An Introduction with R Boca Raton, FL: Chapman & Hall/CRC 2006 ISBN 978-1-58488-474-3
33. PIVUS-Prospective Investigation of the Vasculature in Uppsala Seniors. [ http://www.medsci.uu.se/pivus ]
34. A comparison of three different methods to evaluate endothelium-dependent vasodilation in the elderly. The prospective investigation of the vasculature in Uppsala seniors (PIVUS) study. Lind L, Fors N, Marttala K, Stenborg A, Arterioscler Thromb Vasc Biol 2005 25 1075 1082
35. An investigation of the co-variation in circulating levels of a large number of environmental contaminants. Lampa E, Lind L, Bornefalk-Hermansson A, Salihovic S, van Bavel B, Lind PM, J Expo Sci Env Epid 2012 22 476 482 10.1038/jes.2012.41
36. R Core Team, R: A Language and Environment for Statistical Computing Vienna, Austria: R Foundation for Statistical Computing 2013 http://www.R-project.org/.
37. Kuhn M, Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, caret: Classification and Regression Training 2013 R package version 5.16-04. http://CRAN.R-project.org/package=caret.
38. Sarkar D, Lattice: Multivariate Data Visualization with R New York: Springer 2008 http://lmdvr.r-forge.r-project.org.
39. Sarkar D, Andrews F, latticeExtra: Extra Graphical Utilities Based on Lattice. 2012 R package version 0.6-24. http://CRAN.R-project.org/package=latticeExtra.
40. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Benjamini Y, Hochberg Y, J R Statist Soc B 1995 57 289 300
41. Wickham H, Chang W, devtools: tools to make developing R code easier 2013 R package version 1.4.1. [ http://CRAN.R-project.org/package=devtools ].
42. Harrell Jr FE, rms: Regression modeling strategies. 2013 R package version 4.1-0. [ http://CRAN.R-project.org/package=rms ].
43. Bisphenol a exposure increases liver fat in juvenile fructose-fed Fischer 344 rats. Rönn M, Kullberg J, Karlsson H, Berglund J, Malmberg F, Örberg J, Lind L, Ahlström H, Lind PM, Toxicology 2013 303 125 132 23142792
44. Subchronic toxicity of 3,3',4,4',5-Pentachlorobiphenyl in the Rat I. Clinical, biochemical, hematological, and histopathological changes. Chu I, Villeneuve DC, Yagminas A, Lecavalier P, Poon R, Feeley M, Kennedy SW, Seegal RF, Häkansson H, Ahlborg UG, Valli VE, Toxicol Sci 1994 22 457 468 10.1093/toxsci/22.3.457
45. An environmental wide association study (EWAS) approach to the metabolic syndrome. Lind PM, Risérus U, Salihovic S, van Bavel B, Lind L, Environ Int 2013 55 1 8 23454278
46. RuleFit. [ http://statweb.stanford.edu/~jhf/R-RuleFit.html ]
47. Random forests. Breiman L, Mach Learn 2001 45 5 32 10.1023/A:1010933404324
48. Multivariate adaptive regression splines. Friedman JH, Ann Stat 1991 19 1 141 10.1214/aos/1176347963
49. Logic regression and its extensions. Schwender H, Ruczinski I, Adv Genet 2010 72 25 45 21029847
50. An exploratory technique for investigating large quantities of categorical data. Kass GV, Appl Stat 1980 29 119 127 10.2307/2986296
51. A Lasso for hierarchical interactions. Bien J, Taylor J, Tibshirani R, Ann Stat 2012 41 1111 1141
52. Learning interactions through hierarchical group-lasso regularization. Lim M, Hastie T, 2013 [ http://arxiv.org/abs/1308.2719 ]