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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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry/ edited by Susmita Datta, Bart J. A. Mertens.
其他作者:	Mertens, Bart J. A.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	viii, 295 p. :ill., digital ;24 cm.
內容註:	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
標題:	Mass spectrometry. -
電子資源:	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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