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MALDI-TOF Baseline Drift Removal Using Stochastic Bernstein Approximation

  • Joseph KolibalEmail author
  • Daniel Howard
Open Access
Research Article
Part of the following topical collections:
  1. Advanced Signal Processing Techniques for Bioinformatics

Abstract

Stochastic Bernstein (SB) approximation can tackle the problem of baseline drift correction of instrumentation data. This is demonstrated for spectral data: matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) data. Two SB schemes for removing the baseline drift are presented: iterative and direct. Following an explanation of the origin of the MALDI-TOF baseline drift that sheds light on the inherent difficulty of its removal by chemical means, SB baseline drift removal is illustrated for both proteomics and genomics MALDI-TOF data sets. SB is an elegant signal processing method to obtain a numerically straightforward baseline shift removal method as it includes a free parameter Open image in new window that can be optimized for different baseline drift removal applications. Therefore, research that determines putative biomarkers from the spectral data might benefit from a sensitivity analysis to the underlying spectral measurement that is made possible by varying the SB free parameter. This can be manually tuned (for constant Open image in new window ) or tuned with evolutionary computation (for Open image in new window ).

Keywords

Spectral Data Free Parameter Spectral Measurement Evolutionary Computation Inherent Difficulty 

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Copyright information

© Kolibal and Howard 2006

Authors and Affiliations

  1. 1.Department of Mathematics, College of Science & TechnologyThe University of Southern MississippiHattiesburgUSA
  2. 2.QinetiQ PLCMalvernWorcestershireUnited Kingdom

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