tailieunhanh - Báo cáo hóa học: " MALDI-TOF Baseline Drift Removal Using Stochastic Bernstein Approximation"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: MALDI-TOF Baseline Drift Removal Using Stochastic Bernstein Approximation | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 63582 Pages 1-9 DOI ASP 2006 63582 MALDI-TOF Baseline Drift Removal Using Stochastic Bernstein Approximation Joseph Kolibal1 and Daniel Howard2 1 Department of Mathematics College of Science Technology The University of Southern Mississippi Hattiesburg MS 39406-0001 USA 2 QinetiQ PLC Malvern Worcestershire WR14 3PS United Kingdom Received 7 July 2005 Revised 21 August 2005 Accepted 1 December 2005 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 Ơ x 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 Ơ or tuned with evolutionary computation for Ơ x . Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Each measurement analysis tool for determining the presence and concentration of biomolecules has its particular signal processing challenge. Consider some of these challenges for two of the most powerful tools microarray analysis and spectral analysis. For example the proximity of dots in a microarray can