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Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

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Cover of 'Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry'

Table of Contents

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    Book Overview
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    Chapter 1 Transformation, Normalization, and Batch Effect in the Analysis of Mass Spectrometry Data for Omics Studies
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    Chapter 2 Automated Alignment of Mass Spectrometry Data Using Functional Geometry
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    Chapter 3 The Analysis of Peptide-Centric Mass-Spectrometry Data Utilizing Information About the Expected Isotope Distribution
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    Chapter 4 Probabilistic and Likelihood-Based Methods for Protein Identification from MS/MS Data
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    Chapter 5 An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Proteomic Data Processing
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    Chapter 6 Mass Spectrometry Analysis Using MALDIquant
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    Chapter 7 Model-Based Analysis of Quantitative Proteomics Data with Data Independent Acquisition Mass Spectrometry
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    Chapter 8 The Analysis of Human Serum Albumin Proteoforms Using Compositional Framework
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    Chapter 9 Variability Assessment of Label-Free LC-MS Experiments for Difference Detection
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    Chapter 10 Statistical Approach for Biomarker Discovery Using Label-Free LC-MS Data: An Overview
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    Chapter 11 Bayesian Posterior Integration for Classification of Mass Spectrometry Data
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    Chapter 12 Logistic Regression Modeling on Mass Spectrometry Data in Proteomics Case-Control Discriminant Studies
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    Chapter 13 Robust and Confident Predictor Selection in Metabolomics
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    Chapter 14 On the Combination of Omics Data for Prediction of Binary Outcomes
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    Chapter 15 Statistical Analysis of Lipidomics Data in a Case-Control Study
Attention for Chapter 6: Mass Spectrometry Analysis Using MALDIquant
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Chapter title
Mass Spectrometry Analysis Using MALDIquant
Chapter number 6
Book title
Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry
Published by
Springer International Publishing, December 2016
DOI 10.1007/978-3-319-45809-0_6
Book ISBNs
978-3-31-945807-6, 978-3-31-945809-0
Authors

Sebastian Gibb, Korbinian Strimmer, Gibb, Sebastian, Strimmer, Korbinian

Editors

Susmita Datta, Bart J. A. Mertens

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 20%
Student > Bachelor 3 15%
Researcher 2 10%
Student > Ph. D. Student 1 5%
Other 1 5%
Other 1 5%
Unknown 8 40%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 25%
Medicine and Dentistry 3 15%
Immunology and Microbiology 1 5%
Agricultural and Biological Sciences 1 5%
Chemistry 1 5%
Other 1 5%
Unknown 8 40%