Breast cancer diagnosis from proteomic mass spectrometry data: A comparative evaluation

Statistical Applications in Genetics and Molecular Biology

Volumn 7, Issue 2, 2008, Pages

  Hand, David J ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Classification; Classifier performance; Error rate; Proteomic mass spectra; Sensitivity; Specificity

Indexed keywords

ARTICLE; BREAST TUMOR; CALIBRATION; COMPARATIVE STUDY; EVALUATION; FEMALE; HUMAN; MASS SPECTROMETRY; METABOLISM; METHODOLOGY; PROTEOMICS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

BREAST NEOPLASMS; CALIBRATION; FEMALE; HUMANS; MASS SPECTROMETRY; PROTEOMICS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 58149330446     PISSN: None     EISSN: 15446115     Source Type: Journal    
DOI: 10.2202/1544-6115.1435     Document Type: Article

References (32)

1. Barrett J.H. and Cairns D.A. (2008) Application of the random forest classification method to peaks detected from mass spectrometric proteomic profiles of cancer patients and controls. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 4.
2. Box G.E.P. and Hunter W. (1965) The experimental study of physical mechanisms. Technometrics, 7, 57-71.
3. Cox D.R. (1990) Role of models in statistical analysis. Statistical Science, 5, 169-174.
4. Datta S. (2008) Classification of Breast Cancer versus Normal Samples from Mass Spectrometry Profiles Using Linear Discriminant Analysis of Important Features Selected by Random Forest. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 7.
5. Duin R.P.W. (1996) A note on comparing classifiers. Pattern Recognition Letters, 17, 529-536.
6. Efron B. (2001) Comment on 'Statistical modelling: the two cultures,' by Leo Breiman. Statistical Science, 16, 218-219.
7. Fearn T. (2008) Principal component discriminant analysis. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 6.
8. Gammerman A., Nouretdinov I., Burford B., Chervonenkis A., Vovk V., and Luo Z. (2008) Clinical mass spectrometry proteomic diagnosis by conformal predictors. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 13.
9. Goeman J.K. (2008) Autocorrelated logistic ridge regression for prediction based on proteomics spectra. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 10.
10. Hand D.J. (1985) Artificial Intelligence and Psychiatry, Cambridge: Cambdridge University Press.
11. Hand D.J. (1997) Construction and Assessment of Classification Rules. Chichester: Wiley.
12. Hand D.J. (2006) Classifier technology and the illusion of progress (with discussion). Statistical Science, 21, 1-34.
13. Hand D.J. (2008) Mining the past to determine the future. To appear in International Journal of Forecasting.
14. Hand D.J. (2008b) Measuring classifier performance: beyond the area under the ROC curve. Technical Report. Department of Mathematics, Imperial College, London.
15. Hand D.J., Brentnall A., and Crowder M.J. (2008) Beyond empirical scorecards. Journal of Financial Transformation, 23, 121-128.
16. Hastie T., Tibshirani R., and Friedman J. (2001) The Elements of Statistical Learning Theory. New York: Springer.
17. Hoadley B. (2001) Comment on 'Statistical modelling: the two cultures', by Leo Brieman. Statistical Science, 16, 220-224.
18. Hoefsloot H.C.J., Smit S., and Smilde A.K. (2008) A classification model for the Lieden proteomics competition. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 8.
19. Heidema A.G. and Nagelkerke N. (2008) Developing a discrimination rule between breast cancer patients and controls using breast cancer patients and controls using proteomic mass spectrometric data: a three-step approach. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 5.
20. Jamain A. and Hand D.J. (2008) Mining supervised classification performance studies: a meta-analytic investigation. Journal of Classification, 25, 87-112.
21. Krzanowski W.J. and Hand D.J. (in press) ROC curves for continuous data. London: Chapman and Hall.
22. Kuncheva L.I. (2004) Combining Pattern Classifiers, Methods and Algorithms. Wiley, New Jersey.
23. Lehmann E.L. (1990) Model specification: the views of Fisher and Neyman, and later developments. Statistical Science, 5, 160-168.
24. McLachlan G.J. (1992) Discriminant Analysis and Statistical Pattern Recognition. New York: Wiley.
25. Mertens B.J.A. (2008) Organizing a competition on clinical mass spectrometry based proteomic diagnosis. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 3.
26. Mertens B.J.A. and Hand D.J. (2000) Adjusted estimation for the combination of classifiers. Intelligent Data Analysis, 4, 165-179.
27. Pham T.V., van de Wiel M.A., and Jiminez C.R. (2008) Support vector machine approach to separate control and breast cancer serum samples. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 11.
28. Salzberg S.L. (1997) On comparing classifiers: pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 1, 317-328.
29. Strimenopoulou F. and Brown P.J. (2008) Empirical Bayes logistic regression. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 9.
30. van der Werff M.P.J., Mertens B.J.A., de Noo M.E., Bladergroen M.R., Dalebout H.C., Tollenaar R.A.E.M., and Deelder A.M. (2008) Case-control breast cancner study of MALDI-TOF proteomic mass spectrometry data on serum samples. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 2.
31. Valkenborg D., Van Sanden S., Lin D., Kasim A., Zhu Q., Haldermans P., Jansen I., Shkedy Z., and Burzykowski T. (2008) A cross-validation study to select a classification procedure for clinical diagnosis based on proteomic mass spectrometry. Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 12.
32. Webb A.R. (2002) Statistical Pattern Recognition. 2nd ed. Chichester: Wiley.