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Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach
July-September 2006 (vol. 3 no. 3)
pp. 208-219
ASCII Text
x 
 
Weichuan Yu, Xiaoye Li, Junfeng Liu, Baolin Wu, Kenneth R. Williams, Hongyu Zhao, "Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. 3, pp. 208-219, July-September, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2006.41,
author = {Weichuan Yu and Xiaoye Li and Junfeng Liu and Baolin Wu and Kenneth R. Williams and Hongyu Zhao},
title = {Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {3},
number = {3},
issn = {1545-5963},
year = {2006},
pages = {208-219},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.41},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach
IS - 3
SN - 1545-5963
SP208
EP219
EPD - 208-219
A1 - Weichuan Yu,
A1 - Xiaoye Li,
A1 - Junfeng Liu,
A1 - Baolin Wu,
A1 - Kenneth R. Williams,
A1 - Hongyu Zhao,
PY - 2006
KW - Biomarker discovery
KW - peak identification
KW - multiple peak alignment
KW - scale-space
KW - prior information
KW - energy minimization
KW - parameter optimization.
VL - 3
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
In this paper, we address the multiple peak alignment problem in sequential data analysis with an approach based on the Gaussian scale-space theory. We assume that multiple sets of detected peaks are the observed samples of a set of common peaks. We also assume that the locations of the observed peaks follow unimodal distributions (e.g., normal distribution) with their means equal to the corresponding locations of the common peaks and variances reflecting the extension of their variations. Under these assumptions, we convert the problem of estimating locations of the unknown number of common peaks from multiple sets of detected peaks into a much simpler problem of searching for local maxima in the scale-space representation. The optimization of the scale parameter is achieved using an energy minimization approach. We compare our approach with a hierarchical clustering method using both simulated data and real mass spectrometry data. We also demonstrate the merit of extending the binary peak detection method (i.e., a candidate is considered either as a peak or as a nonpeak) with a quantitative scoring measure-based approach (i.e., we assign to each candidate a possibility of being a peak).

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Index Terms:
Biomarker discovery, peak identification, multiple peak alignment, scale-space, prior information, energy minimization, parameter optimization.
Citation:
Weichuan Yu, Xiaoye Li, Junfeng Liu, Baolin Wu, Kenneth R. Williams, Hongyu Zhao, "Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. 3, pp. 208-219, July-Sept. 2006, doi:10.1109/TCBB.2006.41
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