首页 > 新书资源
新书资源(2010年10月)

Statistical bioinformatics with R / Sunil K. Mathur. — Amsterdam : Academic, c2010. – (58.17115/M432)

Contents

    CONTENTS
    
    PREFACE
    ACKNOWLEDGMENTS
    CHAPTER 1 Introduction 1
    1.1 Statistical Bioinformatics
    1.2 Genetics
    1.3 Chi-Square Test
    1.4 The Cell and Its Function
    1.5 DNA
    1.6 DNA Replication and Rearrangements
    1.7 Transcription and Translation
    1.8 Genetic Code
    1.9 Protein Synthesis
    Exercise 1
    Answer Choices for Questions 1 through 15
    CHAPTER 2 Microarrays
    2.1 Microarray Technology
    2.2 Issues in Microarray
    2.3 Microarray and Gene Expression and Its Uses
    2.4 Proteomics
    Exercise 2
    CHAPTER 3 Probability and Statistical Theory
    3.1 Theory of Probability
    3.2 Mathematical or Classical Probability
    3.3 Sets
    3.4 Combinatorics
    3.5 Laws of Probability
    3.6 Random Variables
    3.7 Measures of Characteristics of a Continuous Probability Distribution
    3.8 Mathematical Expectation
    3.9 Bivariate Random Variable
    3.10 Regression
    3.11 Correlation
    3.12 Law of Large Numbers and Central Limit Theorem
    CHAPTER 4 Special Distributions, Properties, and Applications 83
    4.1 Introduction 83
    4.2 Discrete Probability Distributions 84
    4.3 Bernoulli Distribution 84
    4.4 Binomial Distribution 84
    4.5 Poisson Distribution 87
    4.6 Negative Binomial Distribution 89
    4.7 Geometric Distribution 92
    4.8 Hypergeometric Distribution 94
    4.9 Multinomial Distribution 95
    4.10 Rectangular (or Uniform) Distribution 99
    4.11 Normal Distribution 100
    4.12 Gamma Distribution 107
    4.13 The Exponential Distribution 109
    4.14 Beta Distribution 110
    4.15 Chi-Square Distribution 111
    CHAPTER 5 Statistical Inference and Applications 113
    5.1 Introduction 113
    5.2 Estimation 115
    5.3 Methods of Estimation 121
    5.4 Confidence Intervals 122
    5.5 Sample Size 132
    5.6 Testing of Hypotheses 133
    5.7 Optimal Test of Hypotheses 150
    5.8 Likelihood Ratio Test 156
    CHAPTER 6 Nonparametric Statistics 159
    6.1 Chi-Square Goodness-of-Fit Test 160
    6.2 Kolmogorov Smirnov One-Sample Statistic 163
    6.3 Sign Test 164
    6.4 Wilcoxon Signed-Rank Test 166
    6.5 Two-Sample Test 169
    6.6 The Scale Problem 174
    6.7 Gene Selection and Clustering of Time-Course or Dose-Response Gene Expression Profiles 182
    CHAPTER 7 Bayesian Statistics 189
    7.1 Bayesian Procedures
    7.2 Empirical Bayes Methods
    7.3 Gibbs Sampler
    CHAPTER 8 Markov Chain Monte Carlo 203
    8.1 The Markov Chain
    8.2 Aperiodicity and Irreducibility
    8.3 Reversible Markov Chains
    8.4 MCMC Methods in Bioinformatics
    CHAPTER 9 Analysis of Variance 227
    9.1 One-Way ANOVA
    9.2 Two-Way Classification of ANOVA
    CHAPTER 10 The Design of Experiments 253
    10.1 Introduction 253
    10.2 Principles of the Design of Experiments 255
    10.3 Completely Randomized Design 256
    10.4 Randomized Block Design 262
    10.5 Latin Square Design 270
    10.6 Factorial Experiments 278
    10.7 Reference Designs and Loop Designs 286
    CHAPTER 11 Multiple Testing of Hypotheses 293
    11.1 Introduction 293
    11.2 Type I Error and FDR 294
    11.3 Multiple Testing Procedures 297
    REFERENCES 305
    INDEX 315