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International Journal of Biomedical Imaging
Volume 2010, Article ID 896718, 9 pages
http://dx.doi.org/10.1155/2010/896718
Review Article

Feature Detection Techniques for Preprocessing Proteomic Data

1Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA
2Department of Biostatistics, University at Buffalo, Buffalo, NY 14214, USA
3Department of Biostatistics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA

Received 2 October 2009; Revised 24 December 2009; Accepted 17 February 2010

Academic Editor: Shan Zhao

Copyright © 2010 Kimberly F. Sellers and Jeffrey C. Miecznikowski. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.