Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis

IEEE Transactions on Information Technology in Biomedicine

Volumn 11, Issue 3, 2007, Pages 338-347

  Lu, Chuan ^a   Devos, Andy ^b   Suykens, John A K ^a   Arús, Carles ^c   Van Huffel, Sabine ^b  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

^b Katholieke Universiteit Leuven   (Belgium)

^c UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

Author keywords

Bagging; Kernel based probabilistic classifiers; Magnetic resonance spectroscopy (MRS); Microarray; Sparse Bayesian learning (SBL); Variable selection (VS)

Indexed keywords

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; DIAGNOSIS; MAGNETIC RESONANCE SPECTROSCOPY; MICROARRAYS; ONCOLOGY; SUPPORT VECTOR MACHINES;

BAGGING; KERNEL-BASED PROBABILISTIC CLASSIFIER; SPARSE BAYESIAN LEARNING (SBL); VARAIBLE SELECTION;

BIOMEDICAL ENGINEERING;

TUMOR MARKER; TUMOR PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; DISCRIMINANT ANALYSIS; EVALUATION; GENE EXPRESSION PROFILING; HUMAN; METABOLISM; METHODOLOGY; NEOPLASM; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLUSTER ANALYSIS; DIAGNOSIS, COMPUTER-ASSISTED; DISCRIMINANT ANALYSIS; GENE EXPRESSION PROFILING; HUMANS; LINEAR MODELS; NEOPLASM PROTEINS; NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

EID: 34248589755     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2006.889702     Document Type: Article

References (30)

1. U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine, "Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays," Proc. Natl. Acad. Sci. USA, vol. 96, pp. 6745-6750, 1999.
2. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caliguiri, C. D. Bloomeld, and E. S. Lander, "Molecular classication of cancer: Class discovery and class prediction by gene expression monitoring," Science, vol. 286, pp. 531-537, 1999.
3. S. K. Mukherji, Ed., Clinical Applications of Magnetic Resonance Spectroscopy. New York: Wiley-Liss, 1998.
4. S. J. Nelson, "Multivoxel magnetic resonance spectroscopy of brain tumors," Mol. Cancer Therapeutics, vol. 2, pp. 497-507, 2003.
5. L. M. Fu and E. S. Youn, "Improving reliability of gene selection from microarray functional genomics data," IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 3, pp. 191-196, Sep. 2003.
6. I. Guyon, J.Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn. vol. 46, pp. 389-422, 2002.
7. J. Weston, A. Elisseeff, M. Tipping, and B. Schölkopf, "Use of the zero norm with linear models and kernel methods," J. Mach. Learn. Res., vol. 3, pp. 1439-1461, 2002.
8. A. E. Nikulin, B. Dolenko, T. Bezabeh, and R. L. Somorjai, "Near-optimal region selection for feature space reduction: Novel preprocessing methods for classifying MR spectra," NMR Biomed., vol. 11, pp. 209-216, 1998.
9. R. L. Somorjai, B. Dolenko, A. Nikulina, P. Nickersonb, D. Rushb, A. Shawa, M. Glogowskia, J. Rendella, and R. Deslauriers, "Distinguishing normal from rejecting renal allografts: Application of a three-stage classification strategy," Vibr. Spectrosc, vol. 28, pp. 97-102, 2002.
10. M. Xiong, X. Fang, and J. Zhao, "Biomarker identification by feature wrappers," Genome Res., vol. 11, pp. 1878-1887, 2001.
11. Y. Li, C. Campbell, and M. Tipping, "Bayesian automatic relevance determination algorithms for classifying gene expression data," Bioinformatics, vol. 18, pp. 1332-1339, 2002.
12. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, pp. 211-244, 2001.
13. C. M. Bishop andM. E. Tipping, "Bayesian regression and classification," in Advances in Learning Theory: Methods, Models and Applications, vol. 190 NATO Science Series III: Computer and Systems Sciences, J. A. K. Suykens, et al., Ed. Amsterdam, The Netherlands: IOS Press, 2003, pp. 267-288.
14. C. M. Bishop, Neural Networks for Pattern Recognition. NewYork: Oxford Univ. Press, 1995.
15. M. E. Tipping and A. Faul, "Fast marginal likelihood maximisation for sparse Bayesian models," presented at the 9th Int. Workshop Artif. Intell. Stat., Key West, FL, 2003.
16. V. Vapnik, The Nature of Statistical Learning Theory. NewYork: Springer-Verlag, 1995.
17. J. A. K. Suykens and J.Vandewalle, "Least squares support vector machine classifiers," Neural Process. Lett., vol. 9, no. 3, pp. 293-300, 1999.
18. J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines. Singapore:WOrld Scientific, 2002.
19. T. Van Gestel, J. A. K. Suykens, G. Lanckriet, A. Lambrechts, B.DeMoor, and J.Vandewalle, "A Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel Fisher discriminant analysis," Neural Comput., vol. 14, pp. 1115-1148, 2002.
20. 1H MR spectra," J. Magn. Reson., vol. 173, no. 2, pp. 218-228, 2004.
21. L. Breiman, "Bagging predictors," Mach. Learn., vol. 24, pp. 123-140, 1996.
22. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning - Data Mining, Inference, and Prediction. NewYork: Springer, 2001.
23. T. Hastie and R. Tibshirani, "Classification by pairwise coupling," in Advances in Neural Information Processing Systems, vol. 10, M. I. Jordan, M. J. Kearns, and S. A. Solla, Eds. Cambridge, MA: MIT Press, 1998.
24. J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
25. R. Simon, M. Radmacher, K. Dobbin, and L. McShane, "Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification," J. Natl. Cancer Inst., vol. 95, pp. 14-18, 2003.
26. J. A. Hanley and B. McNeil, "The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve," Radiology, vol. 143, pp. 29-36, 1982.
27. + mouse and induces apoptosis in human colon adenocarcinoma cells via cyclic GMP," Cancer Res., vol. 60, pp. 5151-5163, 2000.
28. G. Karakiulakis, C. Papanikolaou, S. M. Jankovic, A. Aletras, E. Papakonstantinou, E. Vretou, and V. Mirtsou-Fidani, "Increased type IV collagen-degrading activity in metastases originaing from priorary tumors of human colon," Invasion Metastasis, vol. 17, no. 3, pp. 158-168, 1997.
29. V. Govindaraju, K. Young, and A. A. Maudsley, "Proton NMR chemical shifts and coupling constants for brain metabolites," NMR Biomed. vol. 13, pp. 129-153, 2003.
30. 1H MRS of human brain tumour cystic fluid: Contribution of macromolecules," MAGMA, vol. 17, pp. 36-46, 2004.