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Statistical Methods in Molecular Biology

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Cover of 'Statistical Methods in Molecular Biology'

Table of Contents

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    Book Overview
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    Chapter 1 Experimental Statistics for Biological Sciences
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    Chapter 2 Nonparametric Methods for Molecular Biology
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    Chapter 3 Basics of Bayesian Methods
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    Chapter 4 The Bayesian t -Test and Beyond
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    Chapter 5 Sample size and power calculation for molecular biology studies.
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    Chapter 6 Designs for linkage analysis and association studies of complex diseases.
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    Chapter 7 Introduction to Epigenomics and Epigenome-Wide Analysis
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    Chapter 8 Exploration, Visualization, and Preprocessing of High–Dimensional Data
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    Chapter 9 Introduction to the Statistical Analysis of Two-Color Microarray Data
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    Chapter 10 Building networks with microarray data.
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    Chapter 11 Support Vector Machines for Classification: A Statistical Portrait
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    Chapter 12 An Overview of Clustering Applied to Molecular Biology
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    Chapter 13 Hidden Markov Model and Its Applications in Motif Findings
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    Chapter 14 Dimension Reduction for High-Dimensional Data
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    Chapter 15 Introduction to the Development and Validation of Predictive Biomarker Models from High-Throughput Data Sets
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    Chapter 16 Multi-gene Expression-based Statistical Approaches to Predicting Patients’ Clinical Outcomes and Responses
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    Chapter 17 Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs
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    Chapter 18 Statistical Methods for Proteomics
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    Chapter 19 Statistical methods for integrating multiple types of high-throughput data.
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    Chapter 20 A Bayesian Hierarchical Model for High-Dimensional Meta-analysis
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    Chapter 21 Methods for Combining Multiple Genome-Wide Linkage Studies
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    Chapter 22 Improved reporting of statistical design and analysis: guidelines, education, and editorial policies.
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    Chapter 23 Stata Companion
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