Abstract
Mass-Spectrometry (MS) based biological analysis is a powerful approach for discovering novel biomarkers or identifying patterns and associations in biological samples. Each value of a spectrum is composed of two measurements, m/Z (mass to charge ratio) and intensity. Even if data produced by mass spectrometers contains potentially huge amount of information, data are often affected by errors and noise due to sample preparation and instrument approximation. Preprocessing consists of (possibly) eliminating noise from spectra and identifying significant values (peaks). Preprocessing techniques need to be applied before performing analysis: cleaned spectra may then be analyzed by using data mining techniques or can be compared with known spectra in databases. This paper surveys different techniques for spectra preprocessing, working either on a single spectrum, or on an entire data set. We analyze preprocessing techniques aiming to correct intensity and m/Z values in order to: (i) reduce noise, (ii) reduce amount of data, and (iii) make spectra comparable.
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Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its carature. The Canadian Cartographer 10, 112–122 (1973)
Cannataro, M., et al.: Mass Spectrometry Data Analysis for Early Detection of Inherited Breast Cancer. In: WIRN 2004, CIBB Workshop (2004)
Gopalakrishnan, V., William, E., Ranganathan, S., Bowser, R., Cudkowic, M.E., Novelli, M., Lattazi, W., Gambotto, A., Day, B.W.: Proteomic data mining challenges in identification of disease-specific biomarkers from variable resolution mass spectra. In: Proceedings of SIAM Bioinformatics Workshop 2004, Lake Buena Vista, FL, April 2004, pp. 1–10 (2004)
Herath, K.: Effects of ’matched filter’ smoothing as measured by receiver operating characteristic curve. Phys. Med. Biol. 21, 442–446 (1976)
Stepinski, T., Ericsson, L., Vagnhammar, B., Gustafsson, M.: Neural Network Based Classifier for Ultrasonic Resonance Spectra. NDT.net 3(12) (1998)
Wagner, M., Naik, D., Pothen, A.: Protocols for disease classification from mass spectrometry data. Proteomics 3(9), 1692–1698 (2003)
Wallace, W., Kearsley, A., Guttman, C.: An operator-independent approach to mass spectral peak identification and integration. Analytical Chemistry 76, 2446–2452 (2004)
Worsley, K.J., Marrett, S., Neelin, P., Evans, A.C.: Searching scale space for activation in pet images. Human Brain Mapping (4), 74–90 (1996)
Wu, B., Abbott, T., Fishman, D., McMurray, W., Mor, G., Stone, K., Ward, D., Williams, K., Zhao, H.: Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 1(19), 1636–1643 (2003)
Yasui, Y., McLerran, D., Adam, B., Winget, M., Thornquist, M., Feng, Z.: An automated peak identification/calibration procedure for high-dimensional protein measures from mass spectrometers. Journal of Biomedicine and Biotechnology (4), 242–248 (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Cannataro, M., Guzzi, P.H., Mazza, T., Tradigo, G., Veltri, P. (2006). On the Preprocessing of Mass Spectrometry Proteomics Data. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_19
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DOI: https://doi.org/10.1007/11731177_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
Online ISBN: 978-3-540-33184-1
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