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Statistical analysis of proteomics, ...

Mertens, Bart J. A.

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Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry/ edited by Susmita Datta, Bart J. A. Mertens.
other author:	Mertens, Bart J. A.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	viii, 295 p. :ill., digital ;24 cm.
[NT 15003449]:	Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies -- Automated Alignment of Mass Spectrometry Data Using Functional Geometry -- The analysis of peptide-centric mass spectrometry data utilizing information about the expected isotope distribution -- Probabilistic and likelihood-based methods for protein identification from MS/MS data -- An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Data Processing -- Mass Spectrometry Analysis Using MALDIquant -- Model-based analysis of quantitative proteomics data with data independent acquisition mass spectrometry -- The analysis of human serum albumin proteoforms using compositional framework -- Variability Assessment of Label-Free LC-MS Experiments for Difference Detection -- Statistical approach for biomarker discovery using label-free LC-MS data - an overview -- Bayesian posterior integration for classification of mass spectrometry data -- Logistic regression modeling on mass spectrometry data in proteomics case-control discriminant studies -- Robust and confident predictor selection in metabolomics -- On the combination of omics data for prediction of binary Outcomes -- Statistical analysis of lipidomics data in a case-control study.
Contained By:	Springer eBooks
Subject:	Mass spectrometry. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-45809-0
ISBN:	9783319458090

Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry
Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry[electronic resource] /edited by Susmita Datta, Bart J. A. Mertens. - Cham :Springer International Publishing :2017. - viii, 295 p. :ill., digital ;24 cm. - Frontiers in probability and the statistical sciences. - Frontiers in probability and the statistical sciences..

Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies -- Automated Alignment of Mass Spectrometry Data Using Functional Geometry -- The analysis of peptide-centric mass spectrometry data utilizing information about the expected isotope distribution -- Probabilistic and likelihood-based methods for protein identification from MS/MS data -- An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Data Processing -- Mass Spectrometry Analysis Using MALDIquant -- Model-based analysis of quantitative proteomics data with data independent acquisition mass spectrometry -- The analysis of human serum albumin proteoforms using compositional framework -- Variability Assessment of Label-Free LC-MS Experiments for Difference Detection -- Statistical approach for biomarker discovery using label-free LC-MS data - an overview -- Bayesian posterior integration for classification of mass spectrometry data -- Logistic regression modeling on mass spectrometry data in proteomics case-control discriminant studies -- Robust and confident predictor selection in metabolomics -- On the combination of omics data for prediction of binary Outcomes -- Statistical analysis of lipidomics data in a case-control study.

ISBN: 9783319458090

Standard No.: 10.1007/978-3-319-45809-0doiSubjects--Topical Terms:

551172
Mass spectrometry.

LC Class. No.: QP519.9.M3

Dewey Class. No.: 543.65

Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry
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245 0 0 $a Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry $h [electronic resource] / $c edited by Susmita Datta, Bart J. A. Mertens.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a viii, 295 p. : $b ill., digital ; $c 24 cm.
490 1 $a Frontiers in probability and the statistical sciences
505 0 $a Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies -- Automated Alignment of Mass Spectrometry Data Using Functional Geometry -- The analysis of peptide-centric mass spectrometry data utilizing information about the expected isotope distribution -- Probabilistic and likelihood-based methods for protein identification from MS/MS data -- An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Data Processing -- Mass Spectrometry Analysis Using MALDIquant -- Model-based analysis of quantitative proteomics data with data independent acquisition mass spectrometry -- The analysis of human serum albumin proteoforms using compositional framework -- Variability Assessment of Label-Free LC-MS Experiments for Difference Detection -- Statistical approach for biomarker discovery using label-free LC-MS data - an overview -- Bayesian posterior integration for classification of mass spectrometry data -- Logistic regression modeling on mass spectrometry data in proteomics case-control discriminant studies -- Robust and confident predictor selection in metabolomics -- On the combination of omics data for prediction of binary Outcomes -- Statistical analysis of lipidomics data in a case-control study.
650 0 $a Mass spectrometry. $3 551172
650 0 $a Proteomics $x Statistical methods. $3 1559579
650 0 $a Lipids $x Statistical methods. $3 3197768
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650 2 4 $a Biostatistics. $3 1002712
650 2 4 $a Metabolomics. $3 1566310
650 2 4 $a Computational Biology/Bioinformatics. $3 898313
650 2 4 $a Analytical Chemistry. $3 890839
650 2 4 $a Probability and Statistics in Computer Science. $3 891072
700 1 $a Mertens, Bart J. A. $3 3197767
700 1 $a Datta, Susmita. $3 3197766
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950 $a Mathematics and Statistics (Springer-11649)