Probabilistic mixture regression models for alignment of LC-MS data

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 8, Issue 5, 2011, Pages 1417-1424

  Befekadu, Getachew K ^a   Tadesse, Mahlet G ^b   Tsai, Tsung Heng ^c   Ressom, Habtom W ^a  

^a GEORGETOWN UNIVERSITY   (United States)

^b GEORGETOWN UNIVERSITY   (United States)

^c Virginia Polytechnic Institute and State University   (United States)

Author keywords

expectation maximization; Liquid chromatography; mass spectrometry; mixed regression model

Indexed keywords

CONTINUOUS PROFILE; CORRELATION OPTIMIZED WARPING; DATA SETS; DENSITY MODELS; DYNAMIC TIME WARPING; EXPECTATION MAXIMIZATION; EXPECTATION-MAXIMIZATION ALGORITHMS; JOINT PARAMETERS; LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY; MIXED-REGRESSION MODEL; MIXTURE REGRESSION; PEAK INTENSITY; RETENTION TIME;

ALGORITHMS; ALIGNMENT; LIQUID CHROMATOGRAPHY; LIQUIDS; MASS SPECTROMETRY; MAXIMUM PRINCIPLE; MIXTURES;

REGRESSION ANALYSIS;

PROTEIN;

ANIMAL; ARTICLE; BIOLOGY; CHEMICAL MODEL; CHEMISTRY; COMPARATIVE STUDY; FACTUAL DATABASE; HUMAN; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; METHODOLOGY; REGRESSION ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STANDARD;

ANIMALS; CHROMATOGRAPHY, LIQUID; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; HUMANS; MASS SPECTROMETRY; MODELS, CHEMICAL; PROTEINS; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 79960913301     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2010.88     Document Type: Article

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