Titlebook: Statistical Analysis in Proteomics; Klaus Jung Book 2016 Springer Science+Business Media New York 2016 Data analysis.High-throughput data.

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1064-3745 e results.Contains key notes and implementation advice from .This valuable collection aims to provide a collection of frequently used statistical methods in the field of proteomics. Although there is a large overlap between statistical methods for the different ‘omics’ fields, methods for analyzing

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A Nonparametric Bayesian Model for Nested Clusteringl to define clusters. This includes in particular model based clustering, Dirichlet process mixtures, product partition models, and more. We show results for two typical biostatistical inference problems that give rise to clustering.

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Application of Discriminant Analysis and Cross-Validation on Proteomics Data available. In this chapter, different approaches for cross-validation are presented including relevant aspects that should be taken into account to avoid overly optimistic results and the assessment of the statistical significance of cross-validated figures of merit.

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Introduction to Proteomics Technologiessible at last. We present and discuss the basic concepts behind proteomics mass spectrometry and the accompanying topic of protein and peptide separations, with a focus on the properties of datasets emerging from such studies.

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