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

This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Datta, Susmita (Επιμελητής έκδοσης), Mertens, Bart J. A. (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
Σειρά:Frontiers in Probability and the Statistical Sciences
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.