Robust SVM-based biomarker selection with noisy mass spectrometric proteomic data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3907 LNCS, Issue , 2006, Pages 79-90

  Marchiori, Elena ^a   Jimenez, Connie R ^a   West Nielsen, Mikkel ^b   Heegaard, Niels H H ^b  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

^b STATENS SERUM INSTITUT   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMARKERS; DIAGNOSIS; DISEASES; LEARNING SYSTEMS; MASS SPECTROMETRY; PERTURBATION TECHNIQUES; ROBUSTNESS (CONTROL SYSTEMS); VECTORS;

BIOMAKER PATTERNS; COMPUTATIONAL ANALYSIS; MASS SPECTROMETRIC PROTEOMIC DATA; SUPPORT VECTOR MACHINES (SVM);

PROTEINS;

EID: 33745767687     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11732242_8     Document Type: Conference Paper

References (28)

1. A. P. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(6):1145-1159, 1997.
2. N. Cristianini and J. Shawe-Taylor. Support Vector machines. Cambridge Press, 2000.
3. E.P. Diamandis. Analysis of serum proteomic patterns for early cancer diagnosis: Drawing attention to potential problems. Journal of the National Cancer Institute, 96(5):353-356, 2004.
4. H.J. Issaq et al. SELDI-TOF MS for diagnostic proteomics. Anal. Chem., 75(7):148A-155A, 2003.
5. Petricoin E.F. et al. Serum proteomic patterns for detection of prostate cancer. Journal of the National Cancer Institute, 94(20):1576-1578, 2002.
6. Petricoin E.F. et al. Use of proteomic patterns in serum to identify ovarian cancer. The Lancet, 359(9306):572-7, 2002.
7. Qu Y. et al. Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin. Chem, 48(10):1835-43, 2002.
8. Zhu W. et al. Detection of cancer-specific markers amid massive mass spectral data. PNAS, 100(25):14666-14671, 2003.
9. T. Evgeniou, M. Pontil, and A. Elisseeff. Leave one out error, stability, and generalization of voting combinations of classifiers. Mach. Learn., 55(1):71-97, 2004.
10. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Machine Learning, 3:1157-1182, 2003. Special Issue on variable and feature selection.
11. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Mach. Learn., 46(1-3):389-422, 2002.
12. George H. John, Ron Kohavi, and Karl Pfleger. Irrelevant features and the subset selection problem. In International Conference on Machine Learning, pages 121-129, 1994.
13. K. Jong, E. Marchiori, M. Sebag, and A. van der Vaart. Feature selection in proteomic pattern data with support vector machines. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2004.
14. K. Kira and L. A. Rendell. The feature selection problem: Traditional methods and a new algorithm. In Tenth National Conference on artificial intelligence, pages 129-134, 1992.
15. J. Li, Z. Zhang, J. Rosenzweig, Y.Y. Wang, and D.W. Chan. Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clinical Chemistry, 48(8):1296-1304, 2002.
16. H. Lie and editors H. Motoda. Feature Extraction, Construction and Selection: a Data Mining Perspective. International Series in Engineering and Computer Science. Kluwer, 1998.
17. H. Liu, J. Li, and L. Wong. A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Informatics, 13:51-60, 2002.
18. E. Marchiori, N.H.H. Heegaard, M. West-Nielsen, and C.R. Jimenez. Feature selection for classification with proteomic data of mixed quality. In Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pages 385-391, 2005.
19. E. Marshall. Getting the noise out of gene arrays. Science, 306:630-631, 2004. Issue 5696.
20. Il-Seok Oh, Jin-Seon Lee, and Byung-Ro Moon. Local search-embedded genetic algorithms for feature selection. In 16 th International Conference on Pattern Recognition (ICPR'02). IEEE Press, 2002.
21. D.F. Ransohoff. Lessons from controversy: Ovarian cancer screening and serum proteomics. Journal of the National Cancer Institute, 97:315-319, 2005.
22. M.L. Raymer, W.F. Punch, E.D. Goodman, L.A. Kuhn, and A.K. Jain. Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computation, 4(2):164-171, 2000.
23. L. A. Rendell and K. Kira. A practical approach to feature selection. In International Conference on machine learning, pages 249-256, 1992.
24. Michiels S., Koscielny S., and Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. The Lancet, 365(9458):488-92, 2005.
25. V.N. Vapnik. Statistical Learning Theory. John Wiley & Sons, 1998.
26. M. West-Nielsen, E.V. Hogdall, E. Marchiori, C.K. Hogdall, C. Schou, and N.H.H. Heegaard. Sample handling for mass spectrometric proteomic investigations of human sera. Analytical Chemistry, 11(16):5114-5123, 2005.
27. E.P. Xing. Feature selection in microarray analysis. In A Practical Approach to Microarray Data Analysis. Kluwer Academic, 2003.
28. L. Yu and H. Liu. Feature selection for high-dimensional data: A fast correlation-based filter solution. In ICML, pages 856-863, 2003.