A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples

Journal of Biomedical Informatics

Volumn 39, Issue 2, 2006, Pages 227-248

  Shin, Hyunjin ^a   Markey, Mia K ^b  

^a The University of Texas at Austin   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Artificial intelligence; Automatic data processing; Blood; Classification; Computer assisted; Diagnosis; Mass spectrometry; Neoplasms; Pattern recognition; Signal processing; Spectrum analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; BLOOD; COMPUTER AIDED ANALYSIS; DIAGNOSIS; GENES; LEARNING SYSTEMS; MASS SPECTROMETERS; PATTERN RECOGNITION; PROTEINS; SIGNAL PROCESSING; SPECTRUM ANALYSIS; TUMORS;

AUTOMATIC DATA PROCESSING; CLASSIFICATION; COMPUTER-ASSISTED; NEOPLASMS;

MEDICAL COMPUTING;

ARTIFICIAL INTELLIGENCE; BLOOD SAMPLING; CANCER; CANCER DIAGNOSIS; COMPUTER ASSISTED DIAGNOSIS; DECISION SUPPORT SYSTEM; EVALUATION; GENOMICS; INFORMATION PROCESSING; LEARNING; MACHINE; MASS SPECTROMETRY; MEDICAL DECISION MAKING; PATTERN RECOGNITION; PRIORITY JOURNAL; PROTEOMICS; REVIEW; SIGNAL PROCESSING; SPECTROSCOPY; TRAINING;

ARTIFICIAL INTELLIGENCE; BLOOD CHEMICAL ANALYSIS; DECISION SUPPORT SYSTEMS, MANAGEMENT; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; MASS SPECTROMETRY; NEOPLASM PROTEINS; NEOPLASMS; RISK ASSESSMENT; RISK FACTORS; TUMOR MARKERS, BIOLOGICAL;

EID: 33644953280     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2005.04.002     Document Type: Review

References (157)

1. Cancer facts and figures 2004. Atlanta: American Cancer Society; 2004.
2. A. Jemal, R.C. Tiwari, T. Murray, A. Ghafoor, A. Samuels, and E. Ward Cancer statistics CA Cancer J Clin 54 2004 8 29
3. R. Etzioni, N. Urban, S. Ramsey, M. McIntosh, S. Schwartz, and B. Reid The case for early detection [review] [86 refs] Nat Rev 3 2003 Cancer
4. M.T. Fahey, L. Irwig, and P. Macaskill Meta-analysis of pap test accuracy [see comment] Am J Epidemiol 141 1995 680 689
5. B.B. Green, and S.H. Taplin Breast cancer screening controversies J Am Board Fam Pract 16 2003 233 241
6. C.H. Lee Screening mammography: Proven benefit, continued controversy Radiol Clin North Am 40 2002 395 407
7. A.M. Knutzen, and J.J. Gisvold Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions Mayo Clin Proc 68 1993 454 460
8. D.B. Kopans The positive predictive value of mammography Am J Roentgenol 158 1992 521 526
9. L.L. Humphrey, M. Helfand, B.K.S. Chan, and S.H. Woolf Breast cancer screening: a summary of the evidence for the US. Preventive services task force Ann Intern Med 137 2002 347 360
10. J.M.E. Walsh, and J.P. Terdiman Colorectal cancer screening: clinical applications J Am Med Assoc 289 2003 1297 1302
11. J.M.E. Walsh, and J.P. Terdiman Colorectal cancer screening: scientific review J Am Med Assoc 289 2003 1288 1296
12. M. Pignone, M. Rich, S.M. Teutsch, A.O. Berg, and K.N. Lohr Screening for colorectal cancer in adults at average risk: a summary of the evidence for the US. Preventive services task force Ann Intern Med 137 2002 132 141
13. G. Rennert, H.S. Rennert, E. Miron, and Y. Peterburg Population colorectal cancer screening with fecal occult blood test Cancer Epidemiol Biomarkers Prev 10 2001 1165 1168
14. S.W. Vernon Participation in colorectal cancer screening: a review [see comment] J Natl Cancer Inst 89 1997 1406 1422
15. M.E. Peek, and J.H. Han Disparities in screening mammography. Current status, interventions and implications J Gen Intern Med 19 2004 184 194
16. M.K. Brawer Prostate-specific antigen: Current status CA Cancer J Clin 49 1999 264 281
17. L.A. Liotta, M. Ferrari, and E. Petricoin Written in blood Nature 425 2003 905
18. W. Pusch, M.T. Flocco, S.M. Leung, H. Thiele, and M. Kostrzewa Mass spectrometry-based clinical proteomics [review] [68 refs] Pharmacogenomics 4 2003 463 476
19. P.R. Srinivas, S. Srivastava, S. Hanash, and G.L. Wright Jr. Proteomics in early detection of cancer Clin Chem 47 2001 1901 1911
20. J.D. Wulfkuhle, L.A. Liotta, and E.F. Petricoin Proteomic applications for the early detection of cancer Nat Rev Cancer 3 2003 267 275
21. R. Woolas, F. Xu, I. Jacobs, Y. Yu, L. Daly, and A. Berchuck Elevation of multiple serum markers in patients with stage i ovarian cancer J Natl Cancer Inst 85 1993 1748 1751
22. A. Abbott A post-genomic challenge: learning to read patterns of protein synthesis Nature 402 1999 715 720
23. A. Madi, T. Pusztahelyi, M. Punyiczki, and L. Fesus The biology of the post-genomic era: the proteomics Acta Biol Hung 54 2003 1 14
24. M. Verma, G.L. Wright Jr., S.M. Hanash, R. Gopal-Srivastava, and S. Srivastava Proteomic approaches within the nci early detection research network for the discovery and identification of cancer biomarkers Ann NY Acad Sci 945 2001 103 115
25. A.J. Rai, and D.W. CHan Cancer proteomics: serum diagnostics for tumor marker discovery Ann NY Acad Sci 1022 2004 286 294
26. K.D. Rodland Proteomics and cancer diagnosis: the potential of mass spectrometry Clin Biochem 37 2004 579 583
27. T.P. Conrads, M. Zhou, E.F. Petricoin 3rd, L. Liotta, and T.D. Veenstra Cancer diagnosis using proteomic patterns Expert Rev Mol Diagn 3 2003 411 420
28. R.C. Krieg, C.P. Paweletz, L.A. Liotta, and E.F. Petricoin 3rd Clinical proteomics for cancer biomarker discovery and therapeutic targeting Technol Cancer Res Treat 1 2002 263 272
29. E.E. Petricoin, C.P. Paweletz, and L.A. Liotta Clinical applications of proteomics: proteomic pattern diagnostics J Mammary Gland Biol Neoplasia 7 2002 433 440
30. E.F. Petricoin, D.A. Fishman, T.P. Conrads, T.D. Veenstra, and L.A. Liotta Lessons from kitty hawk: from feasibility to routine clinical use for the field of proteomic pattern diagnostics Proteomics 4 2004 2357 2360
31. K.P. Rosenblatt, P. Bryant-Greenwood, J.K. Killian, A. Mehta, D. Geho, and V. Espina Serum proteomics in cancer diagnosis and management Ann Rev Med 55 2004 97 112
32. G. Alexe, S. Alexe, L.A. Liotta, E.F. Petricoin, M. Reiss, and P.L. Hammer Ovarian cancer detection by logical analysis of proteomic data Proteomics 4 2004 766 783
33. T.P. Conrads, V.A. Fusaro, S. Ross, D. Johann, V. Rajapakse, and B.A. Hitt High-resolution serum proteomic features for ovarian cancer detection Endocr Relat Cancer 11 2004 163 178
34. N. Jeffries Performance of a genetic algorithm for mass spectrometry proteomics BMC Bioinformatics 5 2004 180
35. Johann Jr DJ, McGuigan MD, Tomov S, Fusaro VA, Ross S, Conrads TP, et al. Novel approaches to visualization and data mining reveals diagnostic information in the low amplitude region of serum mass spectra from ovarian cancer patients. Dis Markers 2003-2004;19:197-207.
36. K.R. Kozak, M.W. Amneus, S.M. Pusey, F. Su, M.N. Luong, and S.A. Luong Identification of biomarkers for ovarian cancer using strong anion-exchange proteinchips: potential use in diagnosis and prognosis Proc Natl Acad Sci USA 100 2003 12343 12348
37. J. Li, H. Liu, S. Ng, and L. Wong Discovery of significant rules for classifying cancer diagnosis data Bioinformatics 19 2003 93 102
38. L. Li, H. Tang, Z. Wu, J. Gong, M. Gruidl, and J. Zou Data mining techniques for cancer detection using serum proteomic profiling Artif Intell Med 32 2004 71 83
39. E.F. Petricoin, A.M. Ardekani, B.A. Hitt, P.J. Levine, V.A. Fusaro, and S.M. Steinberg Use of proteomic patterns in serum to identify ovarian cancer Lancet 359 2002 572 577
40. R.R. Somorjai, B. Dolenko, and R. Baumgartner Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions Bioinformatics 19 2003 1484 1491
41. J.M. Sorace, and M. Zhan A data review and re-assessment of ovarian cancer serum proteomic profiling BMC Bioinformatics 4 2003
42. B. Wu, T. Abbott, D. Fishman, W. McMurray, G. Mor, and K. Stone Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data Bioinformatics 19 2003 1636 1643
43. W. Zhu, X. Wang, Y. Ma, M. Rao, J. Glimm, and J.S. Kovach Detection of cancer-specific markers amid massive mass spectral data Proc Natl Acad Sci USA 100 2003 14666 14671
44. B.L. Adam, Y. Qu, J.W. Davis, M.D. Ward, M.A. Clements, and L.H. Cazares Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men Cancer Res 62 2002 3609 3614
45. L.H. Cazares, B.L. Adam, M.D. Ward, S. Nasim, P.F. Schellhammer, and O.J. Semmes Normal, benign, preneoplastic, and malignant prostate cells have distinct protein expression profiles resolved by surface enhanced laser desorption/ionization mass spectrometry Clin Cancer Res 8 2002 2541 2552
46. J. Li, N. White, Z. Zhang, J. Rosenzweig, L.A. Mangold, and A.W. Partin Detection of prostate cancer using serum proteomics pattern in a histologically confirmed population [article] J Urol 171 5 2004 1782 1787
47. 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 10 2003 925 946
48. D.K. Ornstein, W. Rayford, V.A. Fusaro, T.P. Conrads, S.J. Ross, and B.A. Hitt Serum proteomic profiling can discriminate prostate cancer from benign prostates in men with total prostate specific antigen levels between 2.5 and 15.0 ng/ml J Urol 172 2004 1302 1305
49. Y. Qu, B.-L. Adam, Y. Yasui, M.D. Ward, L.H. Cazares, and P.F. Schellhammer Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients Clin Chem 48 2002 1835 1843
50. Y. Qu, B.L. Adam, M. Thornquist, J.D. Potter, M.L. Thompson, and Y. Yasui Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data Biometrics 59 2003 143 151
51. J.H. Stone, V.N. Rajapakse, G.S. Hoffman, U. Specks, P.A. Merkel, and R.F. Spiera A serum proteomic approach to gauging the state of remission in wegener's granulomatosis Arthritis Rheum 52 2005 902 910
52. M. Wagner, D. Naik, A. Pothen, S. Kasukurti, R. Devineni, and B.-L. Adam Computational protein biomarker prediction: a case study for prostate cancer BMC Bioinformatics 5 2004 26
53. Y. Yasui, M. Pepe, M.L. Thompson, B.L. Adam, G.L. Wright Jr., and Y. Qu A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection Biostatistics 4 2003 449 463
54. S. Becker, L. Cazares, P. Watson, H. Lynch, O. Semmes, and R. Drake Surfaced-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) differentiation of serum protein profiles of brca-1 and sporadic breast cancer Ann Surg Oncol 11 2004 907 914
55. 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 48 2002 1296 1304
56. A. Vlahou, A. Giannopoulos, B.W. Gregory, T. Manousakas, F.I. Kondylis, and L.L. Wilson Protein profiling in urine for the diagnosis of bladder cancer Clin Chem 50 2004 1438 1441
57. A. Vlahou, P.F. Schellhammer, and G.L. Wright Jr. Application of a novel protein chip mass spectrometry technology for the identification of bladder cancer-associated biomarkers Adv Exp Med Biol 539A 2003 47 60
58. K.A. Baggerly, J.S. Morris, J. Wang, D. Gold, L. Xiao, and K.R. Coombes A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteomics spectra from serum samples Proteomics 3 2003 1667 1672
59. M.J. Campa, M.Z. Wang, B. Howard, M.C. Fitzgerald, and E.F. Patz Jr. Protein expression profiling identifies macrophage migration inhibitory factor and cyclophilin a as potential molecular targets in non-small cell lung cancer Cancer Res 63 2003 1652 1656
60. M. Hilario, A. Kalousis, M. Muller, and C. Pellegrini Machine learning approaches to lung cancer prediction from mass spectra Proteomics 3 2003 1716 1719
61. K.R. Lee, X. Lin, D.C. Park, and S. Eslava Megavariate data analysis of mass spectormetric proteomics data using latent variable projection method Proteomics 3 2003 1680 1686
62. Liu Q, Krishnapuram B, Pratapa P, Liao X, Hartemink A, Carin L. Identification of differentially expressed proteins using MALDI-TOF mass spectra. In: ASILOMAR conference: biological aspects of signal processing; 2003.
63. M.K. Markey, G.D. Tourassi, and C.E.J. Floyd Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer Proteomics 3 2003 1678 1679
64. P. Neville, P. Tan, G. Mann, and R. Wolfinger Generalizable mass spectrometry mining used to identify disease state biomarkers from blood serum Proteomics 3 2003 1710 1715
65. P.V. Purohit, and D.M. Rocke Discriminant models for high-throughput proteomics mass spectrometer data Proteomics 3 2003 1699 1703
66. D. Sidransky, R. Irizarry, J.A. Califano, X. Li, H. Ren, and N. Benoit Serum protein MALDI profiling to distinguish upper aerodigestive tract cancer patients from control subjects J Natl Cancer Inst 95 2003 1711 1717
67. D.J. Slotta, L.S. Heath, N. Ramakrishnan, R. Helm, and M. Potts Clustering mass spectrometry data using order statistics Proteomics 3 2003 1687 1691
68. J.W. Tatay, X. Feng, N. Sobczak, H. Jiang, C. Chen, and R. Kirova Multiple approaches to data-mining of proteomics data based on statistical and pattern classification methods Proteomics 3 2003 1704 1709
69. M. Wagner, D. Naik, and A. Pothen Protocols for disease classification from mass spectrometry data Proteomics 3 2003 1692 1698
70. K. Yanagisawa, B.J. Xu, P.P. Massion, P.H. Larsen, B.C. White, and J.R. Roberts Proteomic patterns of tumour subsets in non-small-cell lung cancer Lancet 362 2003 433 439
71. H. Zhu, C.Y. Yu, and H. Zhang Tree-based disease classification using protein data Proteomics 3 2003 1673 1677
72. T.A. Zhukov, R.A. Johnson, A.B. Cantor, R.A. Clark, and M.S. Tockman Discovery of distinct protein profiles specific for lung tumors and pre-malignat lung lesions by SELDI mass spectrometry Lung Cancer 40 2003 267 279
73. T.C.W. Poon, T.-T. Yip, A.T.C. Chan, C. Yip, V. Yip, and T.S.K. Mok Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes Clin Chem 49 2003 752 760
74. S. Bhattacharyya, E.R. Siegel, G.M. Petersen, S.T. Chari, L.J. Suva, and R.S. Haun Diagnosis of pancreatic cancer using serum proteomic profiling Neoplasia 6 2004 674 686
75. J.M. Koomen, L.N. Shih, K.R. Coombes, D. Li, L.-C. Xiao, and I.J. Fidler Plasma protein profiling for diagnosis of pancreatic cancer reveals the presence of host response proteins Clin Cancer Res 11 2005 1110 1118
76. J.M. Koomen, H. Zhao, D. Li, J. Abbruzzese, K. Baggerly, and R. Kobayashi Diagnostic protein discovery using proteolytic peptide targeting and identification Rapid Commun Mass Spectrom 18 2004 2537 2548
77. J. Koopmann, Z. Zhang, N. White, J. Rosenzweig, N. Fedarko, and S. Jagannath Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry Clin Cancer Res 10 2004 860 868
78. A. Valerio, D. Basso, P. Fogar, M. Falconi, E. Greco, and C. Bassi MALDI-TOF analysis of portal sera of pancreatic cancer patients: identification of diabetogenic and antidiabetogenic peptides Clin Chim Acta 343 2004 119 127
79. A. Valerio, D. Basso, S. Mazza, G. Baldo, A. Tiengo, and S. Pedrazzoli Serum protein profiles of patients with pancreatic cancer and chronic pancreatitis: searching for a diagnostic protein pattern Rapid Commun Mass Spectrom 15 2001 2420 2425
80. Y. Won, H. Song, T.W. Kang, J. Kim, B. Han, and S. Lee Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons Proteomics 3 2003 2310 2316
81. R. Seraglia, E. Ragazzi, S. Vogliardi, G. Allegri, S. Pucciarelli, and M. Agostini Search of plasma markers for colorectal cancer by matrix-assisted laser desorption/ionization mass spectrometry J Mass Spectrom 40 2005 123 126
82. G. Ball, S. Mian, F. Holding, R.O. Allibone, J. Lowe, and S. Ali An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers Bioinformatics (Oxford) 18 2002 395 404
83. K.A. Baggerly, J.S. Morris, and K.R. Coombes Reproducibility of SELDI-TOF protein patterns in serum: comparing data sets from different experiments Bioinformatics 20 2004 777 785
84. E.P. Diamandis Analysis of serum proteomic patterns for early cancer diagnosis: drawing attention to potential problems J Natl Cancer Inst 96 2004 353 356
85. E.P. Diamandis Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations [review] [68 refs] Mol Cell Proteomics 3 2004 367 378
86. E.P. Diamandis Proteomic patterns in biological fluids: do they represent the future of cancer diagnostics Clin Chem 49 2003 1272 1275
87. E.P. Diamandis, and D.-E. van der Merwe Plasma protein profiling by mass spectrometry for cancer diagnosis: opportunities and limitations Clin Cancer Res 11 2005 963 965
88. E.F.I. Petricoin, D.K. Ornstein, C.P. Paweletz, A. Ardekani, P.S. Hackett, and B.A. Hitt Serum proteomic patterns for detection of prostate cancer J Natl Cancer Inst 94 2002 1576 1578
89. K.A. Baggerly, J.S. Morris, S.R. Edmonson, and K.R. Coombes Signal in noise: Evaluating reported reproducibility of serum proteomic tests for ovarian cancer J Natl Cancer Inst 97 2005 307 309
90. A.I. Mehta, S. Ross, M.S. Lowenthal, V. Fusaro, D.A. Fishman, and E.F. Petricoin 3rd Biomarker amplification by serum carrier protein binding Dis Markers 19 2003 1 10
91. L.A. Liotta, M. Lowenthal, A. Mehta, T.P. Conrads, T.D. Veenstra, and D.A. Fishman Importance of communication between producers and consumers of publicly available experimental data J Natl Cancer Inst 97 2005 310 314
92. W.E. Grizzle, S. Meleth, E.F. Petricoin, and L.A. Liotta Clarification in the point/counterpoint discussion related to surface-enhanced laser desorption/ionization time-of-flight mass spectrometric identification of patients with adenocarcinomas of the prostate * proteomic pattern complexity reveals a rich and uncharted continent of biomarkers Clin Chem 50 2004 1475 1477
93. O.J. Semmes, Z. Feng, B.-L. Adam, L.L. Banez, W.L. Bigbee, and D. Campos Evaluation of serum protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for the detection of prostate cancer: I. Assessment of platform reproducibility Clin Chem 51 2005 102 112
94. Grizzle WE, Adam BL, Bigbee WL, Conrads TP, Carroll C, Feng Z, et al. Serum protein expression profiling for cancer detection: validation of a SELDI-based approach for prostate cancer. Dis Markers 2003-2004;19:185-95.
95. M.S. Boguski, and M.W. McIntosh Biomedical informatics for proteomics Nature 422 2003 233
96. K.R. Coombes, H.A. Fritsche Jr., C. Clarke, J.N. Chen, K.A. Baggerly, and J.S. Morris Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization Clin Chem 49 2003 1615 1623
97. Z. Feng, R. Prentice, and S. Srivastava Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistical perspective Pharmacogenomics 5 2004 709 719
98. J. Hu, K.R. Coombes, J.S. Morris, and K.A. Baggerly The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales Brief Funct Genomics Proteomics 3 2005 322 331
99. R. Aebersold, and D.R. Goodlett Mass spectrometry in proteomics Chem Rev 101 2001 269 295
100. R. Aebersold, and M. Mann Mass spectrometry-based proteomics Nature 422 2003 198 207
101. S.P. Gygi, and R. Aebersold Mass spectrometry and proteomics Curr Opin Chem Biol 4 2000 489 494
102. M. Mann, R.C. Hendrickson, and A. Pandey Analysis of proteins and protemes by mass spectrometry Annu Rev Biochem 70 2001 437 473
103. G. Siuzdak Mass spectrometry for biotechnology 1996 Academic Press San Diego, CA
104. J.R. Yates 3rd Mass spectrometry. From genomics to proteomics Trends Genet 16 2000 5 8
105. B.O. Keller, and L. Li Discerning matrix-cluster peaks in matrix-assisted laser desorption/ionization time-of-flight mass spectra of dilute peptide mixtures J Am Soc Mass Spectrom 11 2000 88 93
106. A.N. Krutchinsky, and B.T. Chait On the nature of the chemical noise in MALDI mass spectra J Am Soc Mass Spectrom 13 2002 129 134
107. T.W. Hutchens, and T.T. Yip New desorption strategies for the mass spectrometric analysis of macromolecules Rapid Commun Mass Spectrom 7 1993 576 580
108. H.J. Issaq, T.D. Veenstra, T.P. Conrads, and D. Felschow The SELDI-TOF MS approach to proteomics: protein profiling and biomarker identification Biochem Biophys Res Commun 292 2002 587 592
109. M. Merchant, and S.R. Weinberger Recent advancements in surface-enhanced laser desorption/ionization-time of flight-mass spectrometry Electrophoresis 21 1999 1164 1677
110. N. Tang, P. Tornatore, and S.R. Weinberger Current developments in SELDI affinity technology Mass spectrom Rev 1 2004 34 44
111. W. Wu, W. Hu, and J.J. Kavanagh Proteomics in cancer research Intl J Gynecol Cancer 12 2002 409 423
112. A. Vander, J. Sherman, and D. Luciano Human physiology 8th ed. 2001 McGraw-Hill New York
113. N.L. Anderson, and N.G. Anderson The human plasma proteome: history, character, and diagnostic prospects [erratum appears in mol cell proteomics. 2003 jan;2(1):50] Mol Cell Proteomics 1 2002 845 867
114. K.R. Coombes, S. Tsavachidis, J.S. Morris, K.A. Baggerly, M.C. Hung, and H.M. Kuerer Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform 2004 M.D. Anderson Cancer Center Houston
115. G.A. Satten, S. Datta, H. Moura, A.R. Woolfitt, MdG Carvalho, and G.M. Carlone Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens Bioinformatics 20 2004 3128 3136
116. D.I. Malyarenko, W.E. Cooke, B.-L. Adam, G. Malik, H. Chen, and E.R. Tracy Enhancement of sensitivity and resolution of surface-enhanced laser desorption/ionization time-of-flight mass spectrometric records for serum peptides using time-series analysis techniques Clin Chem 51 2005 65 74
117. Shin H, Koomen J, Baggerly KA, Markey MK. Towards a noise model of MALDI TOF spectra. In: American Association for Cancer Research (AACR) advances in proteomics in cancer research, 2004. Key Biscayne, FL; 2004.
118. F.R. Preparata, and M.I. Shamos Computational geometry: An introduction 1985 Springer New York
119. M.Z. Wang, B. Howard, M.J. Campa, E.F. Patz Jr., and M.C. Fitzgerald Analysis of human serum proteins by liquid phase isoelectric focusing and matrix-assisted laser desorption/ionization-mass spectrometry Proteomics 3 2003 1661 1666
120. M. Anderle, S. Roy, H. Lin, C. Becker, and K. Joho Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum Bioinformatics 446 2004
121. V.J. Barclay Application of wavelet transforms to experimental spectra: Smoothing, denoising, and data set compression Anal Chem 1997 78 90
122. X.-G. Shao, A.K.-M. Leung, and F.-T. Chau Wavelet: a new trend in chemistry Acc Chem Res 36 2003 276 283
123. E.A. Robinson Statistical communication and detection 1967 Griffin London
124. H. Kuerer, K. Coombes, J.X. Chen, L. Clarke, C. Fritsche, and H. Krishnamurthy Association between ductal fluid proteomic expression profiles and the presence of lymph node metastases in women with breast cancer Surgery 136 2004 1061 1069
125. J. Prados, A. Kalousis, J.C. Sanchez, L. Allard, O. Carrette, and M. Hilario Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents Proteomics 4 2004 2320 2332
126. R.O. Duda, P.E. Hart, and D.G. Stork Pattern classification 2nd ed. 2000 Wiley-Interscience New York
127. I. Guyon, and A. Elisseeff An introduction to variable and feature selection J Mach Learn Res 3 2003 1157 1182
128. E.T. Fung, and C. Enderwick Proteinchip clinical proteomics: computational challenges and solutions Biotechniques Suppl. 2002
129. A.K. Jain, R.P.W. Duin, and M. Jianchang Statistical pattern recognition: A review IEEE Trans Pattern Anal Mach Intell 22 2000 4
130. A.L. Blum, and P. Langley Selection of relevant features and examples in machine learning Artif Intell 97 1997 245 271
131. M. Dash, and H. Liu Feature selection for classification Intell Data Anal 1 1997
132. M.A. Hall, and G. Holmes Benchmarking attribute selection techniques for discrete class data mining IEEE Trans Knowl Data Eng 15 2003 1437 1447
133. A. Vlahou, P.F. Schellhammer, S. Mendrinos, K. Patel, F.I. Kondylis, and L. Gong Development of a novel proteomic approach for the detection of transitional cell carcinoma of the bladder in urine Am J Pathol 158 2001 1491 1502
134. T.M. Mitchell Machine learning 1997 WCB/McGraw-Hill Boston
135. C.E. Metz Roc methodology in radiologic imaging Invest Radiol 21 1986 720 733
136. L.J. Lancashire, S. Mian, I.O. Ellis, R.C. Rees, and G.R. Ball Current developments in the analysis of proteomic data: artificial neural network data mining techniques for the identification of proteomic biomarkers related to breast cancer Curr Proteomics 2 2005 15 29
137. N. Cristianini, and B. Scholkopf Support vector machines and kernel methods: the new generation of learning machines AI Magazine 2002
138. N. Cristianini, and J. Shawe-Taylor An introduction to support vector machines and other kernel-based learning methods 1st ed. 2000 Cambridge University Press Cambridge
139. M. Pontil, and A. Verri Properties of support vector machines Neural Comput 10 1998 955 974
140. E. Bauer, and R. Kohavi An empirical comparison of voting classification algorithms: bagging, boosting, and variants Mach Learn 36 1999 105 139
141. L. Breiman Random forests Mach Learn 45 2001 5 32
142. Japkowicz N. Learning from imbalanced data sets: a comparison of various strategies. Technical Report. Menlo Park, CA: AAAI Press; 2000. Report No.: WS-00-05.
143. S.B. Kotsiantis, and P.E. Pintelas Mixture of expert agents for handling imbalanced data sets Ann Math Comput Teleinformatics 1 2003 46 55
144. M.A. Maloof Learning when data sets are imbalanced and when costs are unequal and unknown 2003 Department of Computer Science, Georgetown University Washington, DC
145. Maletic JI, Marcus A. Data cleansing: beyond integrity analysis. In: Information quality (IQ2000); 2000 October 2000. Boston, MA; 2000. p. 200-9.
146. K. Orr Data quality and systems theory Commun ACM 1998 66 71
147. T.C. Redman The impact of poor data quality on the typical enterprise Commun ACM 1998 79 82
148. L.E. Dodd, R.F. Wagner, S.G. Armato 3rd ed., M.F. McNitt-Gray, S. Beiden, and H.P. Chan Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium Acad Radiol 11 2004 462 475
149. J. Han, and M. Kamber Data mining: concepts and techniques 1st ed. 2001 Academic Press San Diego
150. C.E. Brodley, and M.A. Friedl Identifying and eliminating mislabeled training instances The 13th national conference on artificial intelligence 1996 AAAI Press Portland, OR 799 805
151. D. Gamberger, N. Lavrac, and C. Groselj Experiments with noise filtering in a medical domain International conference of machine learning (ICML'99) 1999 Morgan Kaufmann San Francisco, CA 143 151
152. B. Efron, and G. Gong A leisurely look at the bootstrap, the jackknife, and cross-validation Am Statistician 37 1983 36 48
153. B. Efron, and R. Tibshirani Statistical data analysis in the computer age Science 253 1991
154. B. Efron, and R.J. Tibshirani An introduction to the bootstrap 1993 Chapman & Hall New York, NY
155. C.M. Bishop Neural networks for pattern recognition 1995 Oxford University Press New York
156. K. Fukunaga Introduction to statistical pattern recognition 2nd ed. 1990 Academic Press Boston
157. T. Hastie, R. Tibshirani, and J. Friedman The elements of statistical learning 2002 Springer Berlin