Bioinformatics and data mining in proteomics

Expert Review of Proteomics

Volumn 3, Issue 3, 2006, Pages 333-343

  Haoudi, Abdelali ^a   Bensmail, Halima ^b  

^a EASTERN VIRGINIA MEDICAL SCHOOL   (United States)

^b UNIVERSITY OF TENNESSEE   (United States)

Author keywords

Bioinformatics; Cancer; Classification; Clustering; Data mining; Infectious disease; Proteomics

Indexed keywords

PROTEOME;

ACCURACY; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BIOINFORMATICS; BIOLOGICAL WARFARE; CLUSTER ANALYSIS; DATA ANALYSIS; GENETIC ALGORITHM; HUMAN; INFORMATION PROCESSING; PROTEIN ANALYSIS; PROTEIN EXPRESSION; PROTEIN FUNCTION; PROTEOMICS; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; REVIEW; TWO DIMENSIONAL GEL ELECTROPHORESIS;

COMPUTATIONAL BIOLOGY; DATA COLLECTION; DATABASES, PROTEIN; HUMANS; PROTEINS; PROTEOMICS;

EID: 33745582911     PISSN: 14789450     EISSN: 17448387     Source Type: Journal    
DOI: 10.1586/14789450.3.3.333     Document Type: Review

References (46)

1. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature 422, 198-207 (2003).
2. Rai AJ, Chan DW. Cancer proteomics: serum diagnostics for tumor marker discovery. Ann. NY Acad. Sci. 1022, 286-294 (2004).
3. Van den Bergh G, Arckens L. Recent advances in 2D electrophoresis: an array of possibilities. Expert Rev. Proteomics 2, 243-252 (2005).
4. Reyzer ML, Caprioli RM. MALDI mass spectrometry for direct tissue analysis: a new tool for biomarker discovery. J. Proteome Res. 4, 1138-11342 (2005).
5. Wright GL Jr. SELDI proteinchip MS: a platform for biomarker discovery and cancer diagnosis. Expert Rev. Mol. Diagn. 2, 549-63 (2002).
6. Paoletti AC, Zybailov B, Washburn MP. Principles and applications of multidimensional protein identification technology. Expert Rev. Proteomics 3, 275-82 (2004).
7. Wall DB, Berger SJ, Finch JW et al. Continuous sample deposition from reversed-phase liquid chromatography to tracks on a matrix-assisted laser desorption/ionization precoated target for the analysis of protein digests. Electrophoresis 23, 3193-3204 (2002).
8. Bensmail H, Haoudi A. Postgenomics: proteomics and bioinformatics in cancer research. J. Biomed. Biotechnol. 4, 217-230 (2003). Summary of different proteomics and bioinformatics approaches for clustering and classification.
9. Bensmail H, Golek J, Moody MM, Semmes OJ, Haoudi A. A novel approach for clustering proteomics data using Bayesian fast Fourier transform. Bioinformatics 21, 2210-2224 (2005). Discusses the use of a combined method of bioinformatics and engineering to denoise and cluster high-dimensional proteomics data.
10. Bensmail H, Aruna B, Semmes OJ, Haoudi A. Functional clustering algorithm for high-dimensional proteomics data. J. Biomed. Biotechnol. 2005, 80-86 (2005).
11. Neville P, Tan P-Y, Mann G, Wolfinger R. Generalizable mass spectrometry mining used to identify disease state biomarkers from blood serum. Proteomics 3, 1710-1715 (2003).
12. Siuzdak G. Mass Spectrometry for Biotechnology. Academic Press, CA, USA (1996).
13. de Hoffman E, Stroobant V. Mass Spectrometry: Principles and Applications. John Wiley, NY, USA (2002).
14. Baggerly KA, Morris JS, Coombes KR. Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments. Bioinformatics 20, 777-785 (2004).
15. Zhang M, Xie X, Lee AH, Binns CW, Holman CDJ. Body mass index in relation to ovarian cancer survival. Cancer Epidemiol. Biomarkers Prev. 14, 1307-1310 (2005).
16. Zhou X, Gonnet G, Hallett M, Munchbach M, Folkers G, James P. Cell fingerprinting: an approach to classifying cells according to mass profiles of digests of protein extracts. Proteomics 1, 683-690 (2001).
17. Prince JT, Carlson MW, Wang R, Lu P, Marcotte EM. The need for a public proteomics repository. Nat. Biotechnol. 22, 471-472 (2004).
18. Alaiya AA, Franzen B, Auer G, Linder S. Cancer proteomics: from identification of novel markers to creation of artificial learning models for tumor classification. Electrophoresis 21, 1210-1217 (2000).
19. Schmid HR, Schmitter D, Blumer P, Miller M, Vonderschmitt D. Lung tumor cells: a multivariate approach to cell classification using two dimensional protein pattern. Electrophoresis 16, 1961-1968 (1995).
20. Li L, Hang H, Gong J et al. Data mining techniques for cancer detection using serum proteomic profiling. Artif. Intell. Med. 32, 71-83 (2004).
21. Adam BL, Vlahou A, Semmes OJ, Wright GL. Proteomic approaches to biomarker discovery in prostate and bladder cancers. Proteomics 1, 1264-1270 (2001).
22. Li J, Zhang Z, Rosenzweig J, Wang YY, Chan DW. Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin. Chem. 48, 1296-1304 (2002).
23. Woolas RP, Xu FJ, Jacobs IJ et al. Evaluation of multiple serum markers in patients with Stage I ovarian cancer. J. Natl Cancer Inst. 85, 1748-1751 (1993).
24. Kozak KR, Amneus MW, Pusey SM et al. Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: potential use in diagnosis and prognosis. Proc. Natl Acad. Sci. USA 100, 12343-12348 (2003).
25. Petricoin EF Jr, Ardekani AM, Hitt BA et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572-577 (2002).
26. Adam BL, QuY, Davis JW et al. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 62, 3609-3614 (2002).
27. Qu Y, Adam BL, Yasui 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, 1835-1843 (2002).
28. Ball G, Mian S, Holding F et al. 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, 395-404 (2002).
29. Poon TCW, Yip T, Chan ATC et al. Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes. Clin. Chem. 49, 752-760 (2003).
30. Wicker N, Dembele D, Raffelsberger W, Poch O. Density of points clustering, application to transcriptomic data analysis. Nucleic Acids Res. 30, 3992-4000 (2002).
31. Bozdogan H, Haughton DMA. Informational complexity criteria for regression models. Comp. Stat. Data Anal. 28, 51-76 (1998).
32. Akaike H. Factor analysis and AIC. Psychometrika 52, 317-332 (1987).
33. Schwarz G. Estimating the dimension of a model. Ann. Stat. 6, 461-464 (1978).
34. Bozdogan H. Choosing the number of component clusters in the mixture model using a new informational complexity criterion of the inverse fisher information matrix. In: Information and Classification. Opitz O, Lausen B, Klar R (Eds), Springer-Verlag, 40-54 (1993).
35. Cazares LH, Adam BL, Ward MD et al. 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, 2541-2552 (2002).
36. Petricoin EF Jr, Ornstein DK, Paweletz CP et al. Serum proteomic patterns for detection of prostate cancer. J. Natl Cancer Inst. 94, 1576-1578 (2002).
37. Wu B, Abbott T, Fishman D et al. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 19, 1636-1643 (2003).
38. Tibshirani R, Hastie T, Narasimhan B et al. Sample classification from protein mass spectrometry by peak probability contrasts. Bioinformatics 20, 3034-3044 (2004).
39. Malone J, McGarry K, Bowerman C. Performing trend analysis on spatio-temporal proteomics data using differential ratio data mining. Proc. 6th EPSRC PREP Confer. (2004). The only paper that proposes the use of spatiotemporal proteomics rather than analyzing proteomics data as cross-sectional data.
40. Hlavaty JJ, Partin AW, Kusinitz F et al. Mass spectroscopy as a discovery tool for identifying serum markers for prostate cancer. Clin. Chem. 47, 1924-1926 (2001).
41. Watkins B, Szaro R, Ball S et al. Detection of early stage cancer by serum protein analysis. Am. Laboratory 32-36 (2001).
42. Sauter ER, Zhu W, Fan X-J, Wassell RP, Chervoneva I, Bois GCD. Proteomic analysis of nipple aspirate fluid to detect biologic markers of breast cancer. Br. J. Cancer 86, 1440-1443 (2002).
43. Karp NA, Spencer M, Lindsay H, O'dell K, Lilley KS. Impact of replicate types on proteomic expression analysis. J. Proteome Res. 4, 1867-1871 (2005).
44. Bertone P, Kluger Y, Lan N et al. SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics, life sciences. Nucleic Acids Res. 29, 2884-2898 (2001).
45. Coombes KR, Tsavachidis S, Morris J, Baggerly KA, Hung M, Kuerer HM. 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, 4107-4117 (2005). Discusses a similar method of denoising and clusterin spectrum data using the wavelet method (borrowed from engineering).
46. Norton J, Harrison A, Salhi A. Proteomic data mining in cancer. BIOKDD03 Workshop on Data mining in Bioinformatics. DC, USA (2003).