An extended Markov blanket approach to proteomic biomarker detection from high-resolution mass spectrometry data

IEEE Transactions on Information Technology in Biomedicine

Volumn 13, Issue 2, 2009, Pages 195-206

  Oh, Jung Hun ^a   Gurnani, Prem ^b   Schorge, John ^c   Rosenblatt, Kevin P ^d   Gao, Jean X ^a  

^a University of Texas at Arlington   (United States)

^b PERKINELMER INC   (United States)

^c UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^d University of Texas Medical Branch School of Medicine   (United States)

Author keywords

Biomarker; Feature selection; Markov blanket (MB); Matrix assisted laser desorption ionization time of flight (MALDI TOF); Ovarian cancer; Preprocessing

Indexed keywords

ALGORITHMS; DESORPTION; FEATURE EXTRACTION; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; LASERS; MASS SPECTROMETERS; MASS SPECTROMETRY; NEGATIVE IONS; PLATINUM; SPECTRUM ANALYSIS; SUPPORT VECTOR MACHINES;

FEATURE SELECTION; MARKOV BLANKET (MB); MATRIX-ASSISTED LASER DESORPTION/IONIZATION TIME-OF-FLIGHT (MALDI-TOF); OVARIAN CANCER; PREPROCESSING;

BIOMARKERS;

BIOLOGICAL MARKER;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BLOOD; FEMALE; HUMAN; MASS SPECTROMETRY; METHODOLOGY; NORMAL DISTRIBUTION; OVARY TUMOR; PROBABILITY; PROTEIN DATABASE; PROTEIN MICROARRAY; PROTEOMICS; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MARKERS; DATABASES, PROTEIN; FEMALE; HUMANS; MARKOV CHAINS; NORMAL DISTRIBUTION; OVARIAN NEOPLASMS; PROTEIN ARRAY ANALYSIS; PROTEOMICS; SENSITIVITY AND SPECIFICITY; SPECTROMETRY, MASS, MATRIX-ASSISTED LASER DESORPTION-IONIZATION;

EID: 63349090086     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2008.2007909     Document Type: Article

References (35)

1. M. Anderle, S. Roy, H. Lin, C. Becker, and K. Joho, "Quantifying reproducibility for differential proteomics: Noise analysis for protein liquid chromatography-MS of human serum," Bioinformatics, vol. 20, pp. 3575-3582, 2004.
2. 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," Clin. Chem., vol. 48, pp. 1296-1304, 2002.
3. K. P. Rosenblatt, P. Bryant-Greenwood, J. K. Killian, A. Mehta, D. Geho, V. Espina, E. F. Petricoin, and L. A. Liotta, "Serum proteomics in cancer diagnosis and management," Annu. Rev. Med., vol. 55, pp. 97-112, 2004.
4. P. R. Srinivas, S. Srivastava, S. Hanash, and G. L. Wright, "Proteomics in early detection of cancer," Clin. Chem., vol. 47, pp. 1901-1911, 2001.
5. J. D. Wulfkuhle, L. A. Liotta, and E. F. Petricoin, "Proteomic applications for the early detection of cancer," Nat. Rev. Cancer vol. 3, pp. 267-275, 2003.
6. Y. Qu, B. L. Adam, Y. Yasui, M. D. Ward, L. H. Cazares, P. F. Schellhammer, Z. Feng, O. J. Semmes, and G. L. Wright, "Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients," Clin. Chem., vol. 48, pp. 1835-1843, 2002.
7. E. F. Petricoin, A. M. Ardekani, B. A. Hitt, P. J. Levine, V. A. Fusaro, S. M. Steinberg, G. B. Mills, C. Simone, D. A. Fishman, E. C. Kohn, and L. A. Liotta, "Use of proteomic patterns in serum to identify ovarian cancer," Lancet, vol. 359, pp. 572-577, 2002.
8. R. H. Lilien, H. Farid, and B. R. Donald, "Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum," J. Comput. Biol., vol. 10, pp. 925-946, 2003.
9. M. Hilario, A. Kalousis, M. Muller, and C. Pellegrini, "Machine learning approaches to lung cancer prediction from mass-spectra," Proteomics, vol. 3, pp. 1716-1719, 2003.
10. M. F. Lopez, A. Mikulskis, S.Kuzdzal, D. A. Bennett, J.Kelly, E.Golenko, J. DiCesare, E. Denoyer, W. F. Patton, R. Ediger, L. Sapp, T. Ziegert, C. Lynch, S. Kramer, G. R. Whiteley, M. R. Wall, D. P. Mannion, G. Cioppa, J. S. Rakitan, and G. M. Wolfe, "High-resolution serum proteomic profiling of Alzheimer disease samples reveals disease-specific, carrier-protein-bound mass signatures," Clin. Chem., vol. 51, no. 10, pp. 1946-1954, 2005.
11. A. I. Metha, S. Ross, M. S. Lowenthal, V. Fusaro, D. A. Fishman, E. F. Petricoin, and L. A. Liotta, "Biomarker amplification by serum carrier protein binding," Dis. Markers, vol. 19, pp. 1-10, 2003.
12. L. Li, D. M. Umbach, P. Terry, and J. A. Taylor, "Application of the GA/KNN method to SELDI proteomics data," Bioinformatics, vol. 20 pp. 1638-1640, 2004.
13. B. L. Adam, Y. Qu, J. W. Davis, M. D. Ward, M. A. Clements, L. H. Cazares, O. J. Semmes, P. F. Schellhammer, Y. Yasui, Z. Feng, and G. L. Wright, "Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men," Cancer Res., vol. 62, pp. 3609-3614, 2002.
14. A. Vlahou, J. O. Schorge, B.W. Gregory, and R. L. Coleman, "Diagnosis of ovarian cancer using decision tree classification of mass spectral data," J. Biomed. Biotechnol., vol. 5, pp. 308-314, 2003.
15. G. Ball, S. Mian, F. Holding, R. O. Allibone, J. Lowe, S. Ali, G. Li, S. McCardle, I. O. Ellis, C. Creaser, and R. C. Rees, "An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers," Bioinformatics, vol. 18, pp. 395-404, 2002.
16. J. S. Yu, S. Ongarello, R. Fiedler, X. W. Chen, G. Toffolo, C. Cobelli, and Z. Trajanoski, "Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data," Bioinformatics, vol. 21, pp. 2200-2209, 2005.
17. J. Yu and X. W. Chen, "Bayesian neural network approaches to ovarian cnancer identification from high-resolution mass spectrometry data," Bioinformatics, vol. 21, pp. i487-i494, 2005.
18. H.W. Ressom, R. S. Varghese, M. Abdel-Hamid, S. A. L. Eissa, D. Saha, L. Goldman, E. F. Petricoin, T. P. Conrads, T. D. Veenstra, C. A. Loffredo, and R. Goldman, "Analysis of mass spectral serum profiles for biomarker selection," Bioinformatics, vol. 21, pp. 4039-4045, 2005.
19. B. Wu, T. Abbott, D. Fishman, W. McMurray, G. Mor, K. Stone, D.Ward, K. Williams, and H. Zhao, "Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data," Bioinformatics, vol. 19, pp. 1636-1643, 2003.
20. R. C. Bast, "Status of tumor markers in ovarian cancer screening," J. Clin. Oncol., vol. 21, pp. 200-205, 2003.
21. L. M. Knowles, A. Nandi, P. Gurnani, D. S. Miller, S. C. Mok, K. P. Rosenblatt, and J. O. Schorge, "Serum proteomic profiling to predict early relapse in ovarian cancer," Gynecol. Oncol., vol. 96, p. 926, 2005, (Abstract Presented for the Thirty-Sixth Annual Meeting of the Society of Gynecologic Oncologists.).
22. M. K. Parmar, J. A. Ledermann, N. Colombo, A. du Bois, J. F. Delaloye, G. B. Kristensen, S. Wheeler, A. M. Swart, W. Qian, V. Torri, I. Floriani, G. Jayson, A. Lamont, and C. Trope, "Paclitaxel plus platinum-based chemotherapy versus conventional platinum-based chemotherapy in women with relapsed ovarian cancer: The ICON4/ AGO-OVAR-2.2 trial," Lancet, vol. 361, pp. 2099-2106, 2003.
23. M. Bern, D. Goldberg, W. H. McDonald, and J. R. Yates, "Automatic quality assessment of peptide tandem mass spectra," Bioinformatics vol. 20, pp. i49-i54, 2004.
24. D. Koller and M. Sahami, "Toward optimal feature selection," in Proc. 13th Int. Conf. Mach. Learning, Bari, Italy, 1996, pp. 284-292.
25. E. P. Xing and R. M. Karp, "CLIFF: Clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts," Bioinformatics, vol. 17, pp. S306-S315, 2001.
26. Z. Zhu, Y.-S. Ong, and M. Dash, "Markov blanket-embedded genetic algorithm for gene selection," Pattern Recognit., vol. 40, pp. 3236-3248, 2007.
27. U. M. Fayyad and K. B. Irani, "Multi-interval discretization of continuous-valued attributes for classification learning," in Proc. 13th Int. Joint Conf. Artif. Intell., Chambéry, France, 1993, pp. 1022-1029.
28. T. A. Knijnenburg, M. J. T. Reinders, and L. F. A. Wessels, "Artifacts of Markov blanket filtering based on discretized features in small sample size applications," Pattern Recognit. Lett., vol. 27, no. 7, pp. 709-714, 2006.
29. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining Knowl. Discovery, vol. 2, no. 2, pp. 121-167, 1998.
30. I. Guyon, J.Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learning vol. 46, pp. 389-422, 2002.
31. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. New York: Academic, 1990.
32. C.-C. Chang and C.-J. Lin. (2008). LIBSVM: A library for support vector machines, Software [Online]. Available: http://www.csie.ntu.edu.tw/ ~cjlin/libsvm
33. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. San Francisco, CA: Morgan Kaufmann, 2005.
34. J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
35. L. Breiman, "Random forest," Mach. Learning, vol. 45, pp. 5-32, 2001.