Utlization of human expert techniques for detection of low-abundant peaks in high-resolution mass spectra

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn , Issue , 2006, Pages 5798-5801

  Boratyn, Grzegorz M ^a,b   Merchant, Michael L ^b   Klein, Jon B ^b  

^a IEEE   (United States)

^b UNIVERSITY OF LOUISVILLE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN EXPERT TECHNIQUES; MASS SPECTRA ANALYSIS; MATHEMATICAL REPRESENTATION; PEAK DETECTION;

ALGORITHMS; MASS SPECTROMETRY; MATHEMATICAL MODELS; SIGNAL DETECTION;

HUMAN ENGINEERING;

PROTEIN;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; CALIBRATION; COMPUTER PROGRAM; HUMAN; MASS SPECTROMETRY; METHODOLOGY; PEPTIDE MAPPING; PROTEIN MICROARRAY; PROTEOMICS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; CALIBRATION; HUMANS; MASS SPECTROMETRY; MODELS, STATISTICAL; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; PEPTIDE MAPPING; PROTEIN ARRAY ANALYSIS; PROTEINS; PROTEOMICS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE;

EID: 34047168690     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2006.260190     Document Type: Conference Paper

References (15)

1. P. R. Srinivas, S. Srivastava, S. Hanash, and G. L. Wright Jr. Proteomics in early detection of cancer. Clinical Chemistry, 47:1901-1911, 2001.
2. B. L. Adam, Y. Qu, J. Davis, M. Ward, M. Clements, L. Cazares, O. J. Schellhammer, Y. Yasui, Z. Feng, and O. L. Wright Jr. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Research, 62:3609-3614, 2002.
3. T. P. Conrads, M. Zhou, E. F. Petricoin III, L. Liotta, and T. D. Veenstra. Cancer diagnosis using proteomic patterns. Future Drugs, 4:411-420, 2003.
4. E. P. Diamandis. Proteomic patterns in biological fluids: do they represent the future of cancer diagnostics? Clinical Chemistry, 8:1272-1278, 2003.
5. J. D. Wulfkuhle, L. A. Liotta, and E. F. Petricoin. Proteomic applications for the early detection of cancer. Nature Reviews, 3:267-275, 2003.
6. Y. Yasui, M. Pepe, M. L. Thompson, B.-L. Adam, G. L. Wright, Jr., Y. Qu, J. D. Potter, M. Winget, M. Thornquist, and Z. Feng. A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics, 4:449-463, 2003.
7. J. S. Morris, K. R. Coombes, J. Koomen, K. A. Baggerly, and R. Kobayashi. Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics, 21(9):1764-1775, 2005.
8. K. A. Baggerly, J. S. Morris, J. Wang, D. Gold, L. C. Xiao, and K. R. Coombes. A comprehensive approach to the analysis of matrix-assisted laser dessorption/ionization-time of flight proteomics spectra from serum samples. Proteomics, 3:1667-1672, 2003.
9. M. A. Rogers, P. Clarke, J. Noble, N. P. Munro, A. Paul, P. J. Shelby, and R. E. Banks. Proteomic profiling of urinary proteins in renal cancer by syrface enhanced laser desorption ionization and neural-network analysi: identification of key issues affecting potential clinical utility. Cancer Research, 63:6971-6983, 2003.
10. D. Bylund, R. Danielsson, G. Malmquist, and K. E. Markides. Chromatographic augment by wrapping and dynamic programming as a pre-processing tools for parfac modeling of liquid chromatography mass spectrometry data. Journal of Chromatography A, 961:237-244, 2002.
11. J. S. Morris K. A. Baggerly M. C. Hung H. M. Kuerer K. R. Coombes, S. Tsavachidis. 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. Technical report, Department of Biostatistics, University of Texas M. D. Anderson Caner Center, 2004.
12. G. A. Satten, S. Datta, H. Moura, A. R. Woolfitt, M. da G. Carvalho, G. M. Carlone, B. K. De, A. Pavlopoulos, and J. R. Barr. Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens. Bioinformatics, 20:3128-3136, 2004.
13. L. A. Higgins. Mass spectral data processing using a novel peak detection algorithm improves results interpretation. In Proceedings of the 52nd ASMS Conference on Mass Spectrometry and Allied Topics, page available on CD, 2004.
14. M. Wagner, D. Naik, and A. Pothen. Protocols for disease classification from mass spectrometry data. Proteomics, 3(9):1692-1698, 2003.
15. K. R. Coombes, J. M. Koomen, K. A. Baggerly, J. S. Morris, and R. Kobayashi. Understanding the characteristics of mass spectrometry data through the use of simulation. Technical report, Department of Biostatistics, University of Texas M. D. Anderson Caner Center, 2004.