| Probabilistic mixture regression models for alignment of LC-MS data. | |
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MedLine Citation:
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PMID: 20837998 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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Getachew K Befekadu; Mahlet G Tadesse; Tsung-Heng Tsai; Habtom W Ressom |
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Publication Detail:
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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:
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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:
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Created Date: 2011-11-01 Completed Date: 2012-01-25 Revised Date: 2012-09-27 |
Medline Journal Info:
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Nlm Unique ID: 101196755 Medline TA: IEEE/ACM Trans Comput Biol Bioinform Country: United States |
Other Details:
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Languages: eng Pagination: 1417-24 Citation Subset: IM |
Affiliation:
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Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, NW, Washington, DC 20057, USA. gkb8@georgetown.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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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:
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R21 CA130837/CA/NCI NIH HHS; R21 CA130837-02/CA/NCI NIH HHS |
| Chemical | |
Reg. No./Substance:
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0/Proteins |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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