The Challenges in Blood Proteomic Biomarker Discovery

  • Guangxu Jin
  • Xiaobo Zhou
  • Honghui Wang
  • Stephen T. C. Wong
Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)


Although discovering proteomic biomarker by using mass spectrometry technique is promising, its rate of introducing proteomic biomarker approved by the US Food and Drug Administration is falling every year and nearly 1 per year on an average since 1998. Apparently, there is a big gap between biomarker discovery and biomarker validation. Here, we reviewed the challenges appearing in the three key stages for the pipeline of proteomic biomarker, that is, blood sample preparation, bioinformatics algorithms for biomarker candidate discovery, and validation and clinical application of proteomic biomarkers. To analyze and explain the reasons for the gap between biomarker discovery and validation, we covered areas ranging from the techniques/methods used in biomarker discovery and their related biological backgrounds to the existing problems in these techniques/methods.


Feature Selection Linear Discriminant Analysis Discrete Wavelet Transform Feature Subset Peak Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is funded by the Bioinformatics Core Research Grant at The Methodist Research Institute, Cornell University. Dr. Zhou is partially funded by The Methodist Hospital Scholarship Award. He and Dr. Wong are also partially funded by NIH grants R01LM08696, R01LM009161, and R01AG028928. The authors have declared no conflict of interest.


  1. Adam BL, Qu Y et al (2002) Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res 62(13):3609–3614PubMedGoogle Scholar
  2. Ahmed N, Barker G et al (2003) An approach to remove albumin for the proteomic analysis of low abundance biomarkers in human serum. Proteomics 3(10):1980–1987PubMedCrossRefGoogle Scholar
  3. Albrethsen J (2007) Reproducibility in protein profiling by MALDI-TOF mass spectrometry. Clin Chem 53(5):852–858PubMedCrossRefGoogle Scholar
  4. Alfassi ZB (2004) On the normalization of a mass spectrum for comparison of two spectra. J Am Soc Mass Spectrom 15(3):385–387PubMedCrossRefGoogle Scholar
  5. America AH, Cordewener JH (2008) Comparative LC-MS: a landscape of peaks and valleys. Proteomics 8(4):731–749PubMedCrossRefGoogle Scholar
  6. Anderson NL, Anderson NG (2002) The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 1(11):845–867PubMedCrossRefGoogle Scholar
  7. Anderson NL, Polanski M et al (2004) The human plasma proteome: a nonredundant list developed by combination of four separate sources. Mol Cell Proteomics 3(4):311–326PubMedCrossRefGoogle Scholar
  8. Andreev VP, Rejtar T et al (2003) A universal denoising and peak picking algorithm for LC-MS based on matched filtration in the chromatographic time domain. Anal Chem 75(22): 6314–6326PubMedCrossRefGoogle Scholar
  9. Arneberg R, Rajalahti T et al (2007) Pretreatment of mass spectral profiles: application to proteomic data. Anal Chem 79(18):7014–7026PubMedCrossRefGoogle Scholar
  10. Baggerly KA, Morris JS et al (2003) A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteomics spectra from serum samples. Proteomics 3(9):1667–1672PubMedCrossRefGoogle Scholar
  11. Ball G, Mian S et al (2002) An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers. Bioinformatics 18(3):395–404PubMedCrossRefGoogle Scholar
  12. Bensmail H, Golek J et al (2005) A novel approach for clustering proteomics data using Bayesian fast Fourier transform. Bioinformatics 21(10):2210–2224PubMedCrossRefGoogle Scholar
  13. Bhanot G, Alexe G et al (2006) A robust meta-classification strategy for cancer detection from MS data. Proteomics 6(2):592–604PubMedCrossRefGoogle Scholar
  14. Bodovitz S, Joos T (2004) The proteomics bottleneck: strategies for preliminary validation of potential biomarkers and drug targets. Trends Biotechnol 22(1):4–7PubMedCrossRefGoogle Scholar
  15. Bolstad BM, Irizarry RA et al (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185–193PubMedCrossRefGoogle Scholar
  16. Brouwers FM, Petricoin EF III et al (2005) Low molecular weight proteomic information distinguishes metastatic from benign pheochromocytoma. Endocr Relat Cancer 12(2):263–272PubMedCrossRefGoogle Scholar
  17. Bylund D, Danielsson R et al (2002) Chromatographic alignment by warping and dynamic programming as a pre-processing tool for PARAFAC modelling of liquid chromatography-mass spectrometry data. J Chromatogr A 961(2):237–244PubMedCrossRefGoogle Scholar
  18. Callister SJ, Barry RC et al (2006) Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J Proteome Res 5(2):277–286PubMedCrossRefGoogle Scholar
  19. Chen T, Kao MY et al (2001) A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry. J Comput Biol 8(3):325–337PubMedCrossRefGoogle Scholar
  20. Cho SY, Lee EY et al (2005) Efficient prefractionation of low-abundance proteins in human plasma and construction of a two-dimensional map. Proteomics 5(13):3386–3396PubMedCrossRefGoogle Scholar
  21. Coombes KR (2005) Analysis of mass spectrometry profiles of the serum proteome. Clin Chem 51(1):1–2PubMedCrossRefGoogle Scholar
  22. Coombes KR, Fritsche HA Jr et al (2003) Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. Clin Chem 49(10):1615–1623PubMedCrossRefGoogle Scholar
  23. Coombes KR, Tsavachidis S et al (2005) 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. Proteomics 5(16):4107–4117PubMedCrossRefGoogle Scholar
  24. Cox J, Mann M (2007) Is proteomics the new genomics? Cell 130(3):395–398PubMedCrossRefGoogle Scholar
  25. Dancik V, Addona TA et al (1999) De novo peptide sequencing via tandem mass spectrometry. J Comput Biol 6(3–4):327–342PubMedCrossRefGoogle Scholar
  26. Davis MT, Patterson SD (2007) Does the serum peptidome reveal hemostatic dysregulation? Ernst Schering Res Found Workshop 61:23–44PubMedCrossRefGoogle Scholar
  27. Diamandis EP (2003) Point: proteomic patterns in biological fluids: do they represent the future of cancer diagnostics? Clin Chem 49(8):1272–1275PubMedCrossRefGoogle Scholar
  28. Diamandis EP (2004) Analysis of serum proteomic patterns for early cancer diagnosis: drawing attention to potential problems. J Natl Cancer Inst 96(5):353–356PubMedCrossRefGoogle Scholar
  29. Diamond DL, Y Zhang et al (2003) Use of ProteinChip array surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to identify thymosin beta-4, a differentially secreted protein from lymphoblastoid cell lines. J Am Soc Mass Spectrom 14(7):760–765PubMedCrossRefGoogle Scholar
  30. Dijkstra M, Vonk RJ et al (2007) SELDI-TOF mass spectra: a view on sources of variation. J Chromatogr B Analyt Technol Biomed Life Sci 847(1):12–23PubMedCrossRefGoogle Scholar
  31. Ebert MP, Meuer J et al (2004) Identification of gastric cancer patients by serum protein profiling. J Proteome Res 3(6):1261–1266PubMedCrossRefGoogle Scholar
  32. Fenselau C (2007) A review of quantitative methods for proteomic studies. J Chromatogr B Analyt Technol Biomed Life Sci 855(1):14–20PubMedCrossRefGoogle Scholar
  33. Fernandez-de-Cossio J, Gonzalez J et al (1995) A computer program to aid the sequencing of peptides in collision-activated decomposition experiments. Comput Appl Biosci 11(4): 427–434PubMedGoogle Scholar
  34. Fischer B, Roth V et al (2005) NovoHMM: a hidden Markov model for de novo peptide sequencing. Anal Chem 77(22):7265–7273PubMedCrossRefGoogle Scholar
  35. Fischer B, Grossmann J et al (2006) Semi-supervised LC/MS alignment for differential proteomics. Bioinformatics 22(14):e132–e140PubMedCrossRefGoogle Scholar
  36. Frank A, Pevzner P (2005) PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal Chem 77(4):964–973PubMedCrossRefGoogle Scholar
  37. Fung ET, Enderwick C (2002) ProteinChip clinical proteomics: computational challenges and solutions. Biotechniques Suppl:34–38, 40–41Google Scholar
  38. Fushiki T, Fujisawa H et al (2006) Identification of biomarkers from mass spectrometry data using a common peak approach. BMC Bioinformatics 7:358PubMedCrossRefGoogle Scholar
  39. Geho DH, Liotta LA et al (2006) The amplified peptidome: the new treasure chest of candidate biomarkers. Curr Opin Chem Biol 10(1):50–55PubMedCrossRefGoogle Scholar
  40. Geurts P, Fillet M et al (2005) Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics 21(14):3138–3145PubMedCrossRefGoogle Scholar
  41. Gobom J, Mueller M et al (2002) A calibration method that simplifies and improves accurate determination of peptide molecular masses by MALDI-TOF MS. Anal Chem 74(15): 3915–3923PubMedCrossRefGoogle Scholar
  42. Gras R, Muller M et al (1999) Improving protein identification from peptide mass fingerprinting through a parameterized multi-level scoring algorithm and an optimized peak detection. Electrophoresis 20(18):3535–3550PubMedCrossRefGoogle Scholar
  43. Hanash SM, Pitteri SJ et al (2008) Mining the plasma proteome for cancer biomarkers. Nature 452(7187):571–579PubMedCrossRefGoogle Scholar
  44. Hastings CA, Norton SM et al (2002) New algorithms for processing and peak detection in liquid chromatography/mass spectrometry data. Rapid Commun Mass Spectrom 16(5):462–467PubMedCrossRefGoogle Scholar
  45. Hauskrecht M, Pelikan R et al (2005) Feature selection for classification of SELDI-TOF-MS proteomic profiles. Appl Bioinformatics 4(4):227–246PubMedCrossRefGoogle Scholar
  46. Higdon R, Kolker N et al (2004) LIP index for peptide classification using MS/MS and SEQUEST search via logistic regression. OMICS 8(4):357–369PubMedCrossRefGoogle Scholar
  47. Hilario M, Kalousis A et al (2006) Processing and classification of protein mass spectra. Mass Spectrom Rev 25(3):409–449PubMedCrossRefGoogle Scholar
  48. Hingorani SR, Petricoin EF et al (2003) Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse. Cancer Cell 4(6):437–450PubMedCrossRefGoogle Scholar
  49. Hoffmann P, Ji H et al (2001) Continuous free-flow electrophoresis separation of cytosolic proteins from the human colon carcinoma cell line LIM 1215: a non two-dimensional gel electrophoresis-based proteome analysis strategy. Proteomics 1(7):807–818PubMedCrossRefGoogle Scholar
  50. Hortin GL (2006) The MALDI-TOF mass spectrometric view of the plasma proteome and peptidome. Clin Chem 52(7):1223–1237PubMedCrossRefGoogle Scholar
  51. Huang L, Jacob RJ et al (2001) Functional assignment of the 20 S proteasome from Trypanosoma brucei using mass spectrometry and new bioinformatics approaches. J Biol Chem 276(30):28327–28339PubMedCrossRefGoogle Scholar
  52. Itoh SG, Okamoto Y (2007) Effective sampling in the configurational space of a small peptide by the multicanonical-multioverlap algorithm. Phys Rev E Stat Nonlin Soft Matter Phys 76(2, Part 2):026705Google Scholar
  53. Jaitly N, Monroe ME et al (2006) Robust algorithm for alignment of liquid chromatography-mass spectrometry analyses in an accurate mass and time tag data analysis pipeline. Anal Chem 78(21):7397–7409PubMedCrossRefGoogle Scholar
  54. Jirasek A, Schulze G et al (2004) Accuracy and precision of manual baseline determination. Appl Spectrosc 58(12):1488–1499PubMedCrossRefGoogle Scholar
  55. Joos TO, Bachmann J (2005) The promise of biomarkers: research and applications. Drug Discov Today 10(9):615–616PubMedCrossRefGoogle Scholar
  56. Karpievitch YV, Hill EG et al (2007) PrepMS: TOF MS data graphical preprocessing tool. Bioinformatics 23(2):264–265PubMedCrossRefGoogle Scholar
  57. Kim YP, Oh YH et al (2008) Protein kinase assay on peptide-conjugated gold nanoparticles. Biosens Bioelectron 23(7):980–986PubMedCrossRefGoogle Scholar
  58. Lange E, Gropl C et al (2007) A geometric approach for the alignment of liquid chromatography-mass spectrometry data. Bioinformatics 23(13): i273–i281PubMedCrossRefGoogle Scholar
  59. Lee DS, Rudge AD et al (2005) A new model validation tool using kernel regression and density estimation. Comput Methods Programs Biomed 80(1):75–87PubMedCrossRefGoogle Scholar
  60. Lee HJ, Lee EY et al (2006) Biomarker discovery from the plasma proteome using multidimensional fractionation proteomics. Curr Opin Chem Biol 10(1):42–49PubMedCrossRefGoogle Scholar
  61. Li B, Robinson DH et al (1997) Evaluation of properties of apigenin and [G-3H]apigenin and analytic method development. J Pharm Sci 86(6):721–725PubMedCrossRefGoogle Scholar
  62. Li J, Zhang Z et al (2002) Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin Chem 48(8):1296–1304PubMedGoogle Scholar
  63. Li L, Umbach DM et al (2004) Application of the GA/KNN method to SELDI proteomics data. Bioinformatics 20(10):1638–1640PubMedCrossRefGoogle Scholar
  64. Listgarten J, Emili A (2005) Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 4(4): 419–434PubMedCrossRefGoogle Scholar
  65. Listgarten J, Neal RM et al (2007) Difference detection in LC-MS data for protein biomarker discovery. Bioinformatics 23(2): e198–e204PubMedCrossRefGoogle Scholar
  66. Liu H, Li J et al (2002) A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Inform 13:51–60PubMedGoogle Scholar
  67. Ludwig JA, Weinstein JN (2005) Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 5(11):845–856PubMedCrossRefGoogle Scholar
  68. Ma B, Zhang K et al (2003) PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun Mass Spectrom 17(20):2337–2342PubMedCrossRefGoogle Scholar
  69. Mackey AJ, Haystead TA et al (2002) Getting more from less: algorithms for rapid protein identification with multiple short peptide sequences. Mol Cell Proteomics 1(2):139–147PubMedCrossRefGoogle Scholar
  70. Malyarenko DI, Cooke WE et al (2005) 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(1):65–74PubMedCrossRefGoogle Scholar
  71. Marcuson R, Burbeck SL et al (1982) Normalization and reproducibility of mass profiles in the detection of individual differences from urine. Clin Chem 28(6):1346–1348PubMedGoogle Scholar
  72. McGuire JN, Overgaard J et al (2008) Mass spectrometry is only one piece of the puzzle in clinical proteomics. Brief Funct Genomic Proteomic 7(1):74–83PubMedCrossRefGoogle Scholar
  73. Miklos GL, Maleszka R (2001) Integrating molecular medicine with functional proteomics: realities and expectations. Proteomics 1(1):30–41PubMedCrossRefGoogle Scholar
  74. Mueller LN, Rinner O et al (2007) SuperHirn – a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics 7(19):3470–3480PubMedCrossRefGoogle Scholar
  75. Ng JK, Ajikumar PK et al (2007) Spatially addressable protein array: ssDNA-directed assembly for antibody microarray. Electrophoresis 28(24):4638–4644PubMedCrossRefGoogle Scholar
  76. Pantaleo MA, Nannini M et al (2008) Conventional and novel PET tracers for imaging in oncology in the era of molecular therapy. Cancer Treat Rev 34(2):103–121PubMedCrossRefGoogle Scholar
  77. Park T, Yi SG et al (2003) Evaluation of normalization methods for microarray data. BMC Bioinformatics 4:33PubMedCrossRefGoogle Scholar
  78. Perkins DN, Pappin DJ et al (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20(18):3551–3567PubMedCrossRefGoogle Scholar
  79. Perrin, C, Walczak B et al (2001) The use of wavelets for signal denoising in capillary electrophoresis. Anal Chem 73(20):4903–4917PubMedCrossRefGoogle Scholar
  80. Petricoin EF, Liotta LA (2003) Mass spectrometry-based diagnostics: the upcoming revolution in disease detection. Clin Chem 49(4):533–534PubMedCrossRefGoogle Scholar
  81. Petricoin EF III, Ornstein DK et al (2002a) Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst 94(20):1576–1578PubMedGoogle Scholar
  82. Petricoin EF, Ardekani AM et al (2002b) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359(9306):572–577PubMedCrossRefGoogle Scholar
  83. Petricoin EF, Belluco C et al (2006) The blood peptidome: a higher dimension of information content for cancer biomarker discovery. Nat Rev Cancer 6(12):961–967PubMedCrossRefGoogle Scholar
  84. Pitzer E, Masselot A et al (2007) Assessing peptide de novo sequencing algorithms performance on large and diverse data sets. Proteomics 7(17):3051–3054PubMedCrossRefGoogle Scholar
  85. Poon TC, Yip TT et al (2003) Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes. Clin Chem 49(5):752–760PubMedCrossRefGoogle Scholar
  86. Powell K (2003) Proteomics delivers on promise of cancer biomarkers. Nat Med 9(8):980PubMedCrossRefGoogle Scholar
  87. Prados J, Kalousis A et al (2004) Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents. Proteomics 4(8):2320–2332PubMedCrossRefGoogle Scholar
  88. Prince JT, Marcotte EM (2006) Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. Anal Chem 78(17):6140–6152PubMedCrossRefGoogle Scholar
  89. Qu Y, Adam BL et al (2002) Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin Chem 48(10):1835–1843PubMedGoogle Scholar
  90. Radhakrishnan R, Solomon M et al (2008) Tissue microarray – a high-throughput molecular analysis in head and neck cancer. J Oral Pathol Med 37(3):166–176PubMedCrossRefGoogle Scholar
  91. Rai AJ, Zhang Z et al (2002) Proteomic approaches to tumor marker discovery. Arch Pathol Lab Med 126(12):1518–1526PubMedGoogle Scholar
  92. Ransohoff DF (2005) Bias as a threat to the validity of cancer molecular-marker research. Nat Rev Cancer 5(2):142–149PubMedCrossRefGoogle Scholar
  93. Rejtar T, Chen HS et al (2004) Increased identification of peptides by enhanced data processing of high-resolution MALDI TOF/TOF mass spectra prior to database searching. Anal Chem 76(20):6017–6028PubMedCrossRefGoogle Scholar
  94. Resing KA, Meyer-Arendt K et al (2004) Improving reproducibility and sensitivity in identifying human proteins by shotgun proteomics. Anal Chem 76(13):3556–3568PubMedCrossRefGoogle Scholar
  95. Ressom HW, Varghese RS et al (2005) Analysis of mass spectral serum profiles for biomarker selection. Bioinformatics 21(21):4039–4045PubMedCrossRefGoogle Scholar
  96. Ressom HW, Varghese RS et al (2007) Peak selection from MALDI-TOF mass spectra using ant colony optimization. Bioinformatics 23(5):619–626PubMedCrossRefGoogle Scholar
  97. Ressom HW, Varghese RS et al (2008) Classification algorithms for phenotype prediction in genomics and proteomics. Front Biosci 13:691–708PubMedCrossRefGoogle Scholar
  98. Rietjens IM, Steensma A et al (1995) Comparative biotransformation of hexachlorobenzene and hexafluorobenzene in relation to the induction of porphyria. Eur J Pharmacol 293(4):293–299PubMedCrossRefGoogle Scholar
  99. Rifai N, Gillette MA et al (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24(8):971–983PubMedCrossRefGoogle Scholar
  100. Rogers MA, Clarke P et al (2003) Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis: identification of key issues affecting potential clinical utility. Cancer Res 63(20):6971–6983PubMedGoogle Scholar
  101. Rosty C, Christa L et al (2002) Identification of hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein I as a biomarker for pancreatic ductal adenocarcinoma by protein biochip technology. Cancer Res 62(6):1868–1875PubMedGoogle Scholar
  102. Sawyers CL (2008) The cancer biomarker problem. Nature 452(7187):548–552PubMedCrossRefGoogle Scholar
  103. Shackman JG, Watson CJ et al (2004) High-throughput automated post-processing of separation data. J Chromatogr A 1040(2):273–282PubMedCrossRefGoogle Scholar
  104. Shen S, Zhang PS et al (2003) Analysis of protein tyrosine kinase expression in melanocytic lesions by tissue array. J Cutan Pathol 30(9):539–547PubMedCrossRefGoogle Scholar
  105. Shevchenko A, Sunyaev S et al (2001) Charting the proteomes of organisms with unsequenced genomes by MALDI-quadrupole time-of-flight mass spectrometry and BLAST homology searching. Anal Chem 73(9):1917–1926PubMedCrossRefGoogle Scholar
  106. Shimizu A, Nakanishi T et al (2006) Detection and characterization of variant and modified structures of proteins in blood and tissues by mass spectrometry. Mass Spectrom Rev 25(5):686–712PubMedCrossRefGoogle Scholar
  107. Shin YK, Lee HJ et al (2006) Proteomic analysis of mammalian basic proteins by liquid-based two-dimensional column chromatography. Proteomics 6(4):1143–1150PubMedCrossRefGoogle Scholar
  108. Silva JC, Denny R et al (2005) Quantitative proteomic analysis by accurate mass retention time pairs. Anal Chem 77(7):2187–2200PubMedCrossRefGoogle Scholar
  109. Simpson RJ, Bernhard OK et al (2008) Proteomics-driven cancer biomarker discovery: looking to the future. Curr Opin Chem Biol 12(1):72–77PubMedCrossRefGoogle Scholar
  110. Steeves JB, Gagne HM et al (2000) Normalization of residual ions after removal of the base peak in electron impact mass spectrometry. J Forensic Sci 45(4):882–885PubMedGoogle Scholar
  111. Stoll D, Templin MF et al (2002) Protein microarray technology. Front Biosci 7:c13–c32PubMedCrossRefGoogle Scholar
  112. Stolt R, Torgrip RJ et al (2006) Second-order peak detection for multicomponent high-resolution LC/MS data. Anal Chem 78(4):975–983PubMedCrossRefGoogle Scholar
  113. Su LK (2003) Co-immunoprecipitation of tumor suppressor protein-interacting proteins. Methods Mol Biol 223:135–140PubMedGoogle Scholar
  114. Tam SW, Pirro J et al (2004) Depletion and fractionation technologies in plasma proteomic analysis. Expert Rev Proteomics 1(4):411–420PubMedCrossRefGoogle Scholar
  115. Tan CS, Ploner A et al (2006) Finding regions of significance in SELDI measurements for identifying protein biomarkers. Bioinformatics 22(12):1515–1523PubMedCrossRefGoogle Scholar
  116. Tang HY, Ali-Khan N et al (2005) A novel four-dimensional strategy combining protein and peptide separation methods enables detection of low-abundance proteins in human plasma and serum proteomes. Proteomics 5(13):3329–3342PubMedCrossRefGoogle Scholar
  117. Taylor JA, Johnson RS (1997) Sequence database searches via de novo peptide sequencing by tandem mass spectrometry. Rapid Commun Mass Spectrom 11(9):1067–1075PubMedCrossRefGoogle Scholar
  118. Taylor JA, Johnson RS (2001) Implementation and uses of automated de novo peptide sequencing by tandem mass spectrometry. Anal Chem 73(11):2594–2604PubMedCrossRefGoogle Scholar
  119. Thomas TM, Shave EE et al (2002) Preparative electrophoresis: a general method for the purification of polyclonal antibodies. J Chromatogr A 944(1–2):161–168PubMedCrossRefGoogle Scholar
  120. Tibshirani R, Hastie T et al (2004) Sample classification from protein mass spectrometry, by ‘peak probability contrasts’. Bioinformatics 20(17):3034–3044PubMedCrossRefGoogle Scholar
  121. Villanueva J, Philip J et al (2004) Serum peptide profiling by magnetic particle-assisted, automated sample processing and MALDI-TOF mass spectrometry. Anal Chem 76(6):1560–1570PubMedCrossRefGoogle Scholar
  122. Vlahou A, Laronga C et al (2003) A novel approach toward development of a rapid blood test for breast cancer. Clin Breast Cancer 4(3):203–209PubMedCrossRefGoogle Scholar
  123. Wagner M, Naik D et al (2003) Protocols for disease classification from mass spectrometry data. Proteomics 3(9):1692–1698PubMedCrossRefGoogle Scholar
  124. Wang K, Johnson A et al (2005) TSE clearance during plasma products separation process by Gradiflow(TM). Biologicals 33(2):87–94PubMedCrossRefGoogle Scholar
  125. Wang MZ, Howard B et al (2003) Analysis of human serum proteins by liquid phase isoelectric focusing and matrix-assisted laser desorption/ionization-mass spectrometry. Proteomics 3(9):1661–1666PubMedCrossRefGoogle Scholar
  126. Wang P, Tang H et al (2006) Normalization regarding non-random missing values in high-throughput mass spectrometry data. Pac Symp Biocomput 315–326Google Scholar
  127. Wang P, Tang H et al (2007) A statistical method for chromatographic alignment of LC-MS data. Biostatistics 8(2):357–367PubMedCrossRefGoogle Scholar
  128. Weissleder R, Pittet MJ (2008) Imaging in the era of molecular oncology. Nature 452(7187): 580–589PubMedCrossRefGoogle Scholar
  129. Whelan RJ, Sunahara RK et al (2004) Affinity assays using fluorescence anisotropy with capillary electrophoresis separation. Anal Chem 76(24):7380–7386PubMedCrossRefGoogle Scholar
  130. Won Y, Song HJ et al (2003) Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons. Proteomics 3(12):2310–2316PubMedCrossRefGoogle Scholar
  131. Wu B, Abbott T et al (2003) Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 19(13):1636–1643PubMedCrossRefGoogle Scholar
  132. Yasui Y, Pepe M et al (2003) A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics 4(3):449–463PubMedCrossRefGoogle Scholar
  133. Yewdell JW (2003) Immunology. Hide and seek in the peptidome. Science 301(5638):1334–1335Google Scholar
  134. Yu JS, Ongarello S et al (2005) Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data. Bioinformatics 21(10):2200–2209PubMedCrossRefGoogle Scholar
  135. Zhang J, He S et al (2008) PeakSelect: preprocessing tandem mass spectra for better peptide identification. Rapid Commun Mass Spectrom 22(8):1203–1212PubMedCrossRefGoogle Scholar
  136. Zhang X, Lu X et al (2006) Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics 7:197PubMedCrossRefGoogle Scholar
  137. Zhukov TA, Johanson RA et al (2003) Discovery of distinct protein profiles specific for lung tumors and pre-malignant lung lesions by SELDI mass spectrometry. Lung Cancer 40(3):267–279PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Guangxu Jin
  • Xiaobo Zhou
    • 1
  • Honghui Wang
  • Stephen T. C. Wong
  1. 1.HoustonUSA

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