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Statistical analysis of proteomics, ...
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Datta, Susmita.
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 spectrometryedited by Susmita Datta, Bart J. A. Mertens.
other author:
Datta, Susmita.
Published:
Cham :Springer International Publishing :2017.
Description:
viii, 295 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Mass spectrometry.
Online resource:
http://dx.doi.org/10.1007/978-3-319-45809-0
ISBN:
9783319458090$q(electronic bk.)
Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry
Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry
[electronic resource] /edited by Susmita Datta, Bart J. A. Mertens. - Cham :Springer International Publishing :2017. - viii, 295 p. :ill., digital ;24 cm. - Frontiers in probability and the statistical sciences. - Frontiers in probability and the statistical sciences..
Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies -- Automated Alignment of Mass Spectrometry Data Using Functional Geometry -- The analysis of peptide-centric mass spectrometry data utilizing information about the expected isotope distribution -- Probabilistic and likelihood-based methods for protein identification from MS/MS data -- An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Data Processing -- Mass Spectrometry Analysis Using MALDIquant -- Model-based analysis of quantitative proteomics data with data independent acquisition mass spectrometry -- The analysis of human serum albumin proteoforms using compositional framework -- Variability Assessment of Label-Free LC-MS Experiments for Difference Detection -- Statistical approach for biomarker discovery using label-free LC-MS data - an overview -- Bayesian posterior integration for classification of mass spectrometry data -- Logistic regression modeling on mass spectrometry data in proteomics case-control discriminant studies -- Robust and confident predictor selection in metabolomics -- On the combination of omics data for prediction of binary Outcomes -- Statistical analysis of lipidomics data in a case-control study.
ISBN: 9783319458090$q(electronic bk.)
Standard No.: 10.1007/978-3-319-45809-0doiSubjects--Topical Terms:
194021
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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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.
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Mathematics and Statistics (Springer-11649)
based on 0 review(s)
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EB QP519.9.M3 S797 2017
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1 records • Pages 1 •
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http://dx.doi.org/10.1007/978-3-319-45809-0
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