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2012
A. - C. Hauschild, Baumbach, J. I., and Baumbach, J., Integrated statistical learning of metabolic ion mobility spectrometry profiles for pulmonary disease identification, vol. 11, pp. 2733-2744, 2012.
Bader S, Urfer W and Baumbach JI (2008). Preprocessing of ion mobility spectra by lognormal detailing and wavelet transform. Int. J. Ion Mobility Spectrom. 11: 43-49. http://dx.doi.org/10.1007/s12127-008-0005-6   Baumbach J, Bunkowski A, Lange S, Oberwahrenbrock T, et al. (2007). IMS2 - An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath. J. Integr. Bioinform. 4: 75.   Baumbach JI (2009). Ion mobility spectrometry coupled with multi-capillary columns for metabolic profiling of human breath. J. Breath Res. 3: 1-16. http://dx.doi.org/10.1088/1752-7155/3/3/034001 PMid:21383463   Bessa V, Darwiche K, Teschler H, Sommerwerck U, et al. (2011). Detection of volatile organic compounds (VOCs) in exhaled breath of patients with chronic obstructive pulmonary disease (COPD) by ion mobility spectrometry. Int. J. Ion Mobility Spectrom. 14: 7-13. http://dx.doi.org/10.1007/s12127-011-0060-2   Boser BE, Guyon IM and Vapnik VN (1992). A Training Algorithm for Optimal Margin Classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory. ACM Press, New York, 144-152. http://dx.doi.org/10.1145/130385.130401   Dimitriadou E, Hornik K, Leisch F, Meyer D, et al. (2011). Misc Functions of the Department of Statistics (e1071), TU Wien Version 1.5-27. Available at [http://cran.r-project.org/web/packages/e1071/index.html]. Accessed September, 2011   Finthammer M, Beierle C, Fisseler J, Kern-Isberner G, et al. (2010). Probabilistic relational learning for medical diagnosis based on ion mobility spectrometry. Int. J. Ion Mobility Spectrom. 80: 365-375.   Hastie T, Tibshirani R and Friedman JH (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd end. Springer, New York.   Langley P and Sage S (1994). Induction of Selective Bayesian Classifiers. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, Seattle, 399-406.   Liaw A and Wiener M (2002). Classification and regression by random forest. R News 2/3: 18-22.   R Development Core Team (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Available at [http://www.r-project.org]. Accessed April, 2011.   Rabe KF, Hurd S, Anzueto A, Barnes PJ, et al. (2007). Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am. J. Respir. Crit. Care Med. 176: 532-555. http://dx.doi.org/10.1164/rccm.200703-456SO PMid:17507545   Robin X, Turck N, Hainard A, Tiberti N, et al. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12: 77. http://dx.doi.org/10.1186/1471-2105-12-77 PMid:21414208 PMCid:3068975   Therneau TM and Atkinson B (2011). rpart: Recursive Partitioning. R package version 3.1-50.   Venables WN and Ripley BD (2002). Modern Applied Statistics with S. 4th edn. Springer.   Weihs C, Ligges U, Luebke K and Raabe N (2005). klaR Analyzing German Business Cycles. In: Data Analysis and Decision Support (Baier D, Decker R and Schmidt-Thieme L, eds.). Springer-Verlag, Berlin, 335-343.   Westhoff M, Litterst P, Maddula S, Bödeker B, et al. (2011). Statistical and bioinformatical methods to differentiate chronic obstructive pulmonary disease (COPD) including lung cancer from healthy control by breath analysis using ion mobility spectrometry. Int. J. Ion Mobility Spectrom. 14: 139-149. http://dx.doi.org/10.1007/s12127-011-0081-x   World Health Organization (2008). The Global Burden of Disease, 2004 Update. Available at [http://www.who.int/healthinfo/global_burden_disease/en/]. Accessed December 2011.   Young RP, Hopkins RJ, Christmas T, Black PN, et al. (2009). COPD prevalence is increased in lung cancer, independent of age, sex and smoking history. Eur. Respir. J. 34: 380-386. http://dx.doi.org/10.1183/09031936.00144208 PMid:19196816