Document Detail


Probabilistic mixture regression models for alignment of LC-MS data.
MedLine Citation:
PMID:  20837998     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
A novel framework of a probabilistic mixture regression model (PMRM) is presented for alignment of liquid chromatography-mass spectrometry (LC-MS) data with respect to retention time (RT) points. The expectation maximization algorithm is used to estimate the joint parameters of spline-based mixture regression models and prior transformation density models. The latter accounts for the variability in RT points and peak intensities. The applicability of PMRM for alignment of LC-MS data is demonstrated through three data sets. The performance of PMRM is compared with other alignment approaches including dynamic time warping, correlation optimized warping, and continuous profile model in terms of coefficient variation of replicate LC-MS runs and accuracy in detecting differentially abundant peptides/proteins.
Authors:
Getachew K Befekadu; Mahlet G Tadesse; Tsung-Heng Tsai; Habtom W Ressom
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM     Volume:  8     ISSN:  1557-9964     ISO Abbreviation:  IEEE/ACM Trans Comput Biol Bioinform     Publication Date:    2011 Sep-Oct
Date Detail:
Created Date:  2011-11-01     Completed Date:  2012-01-25     Revised Date:  2014-09-21    
Medline Journal Info:
Nlm Unique ID:  101196755     Medline TA:  IEEE/ACM Trans Comput Biol Bioinform     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1417-24     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Animals
Chromatography, Liquid / methods*,  standards
Computational Biology / methods*
Databases, Factual
Humans
Mass Spectrometry / methods*,  standards
Models, Chemical
Proteins / chemistry*
Regression Analysis*
Reproducibility of Results
Sensitivity and Specificity
Grant Support
ID/Acronym/Agency:
R21 CA130837/CA/NCI NIH HHS; R21 CA130837/CA/NCI NIH HHS; R21 CA130837-02/CA/NCI NIH HHS
Chemical
Reg. No./Substance:
0/Proteins
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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