Statistical Bioinformatics provides a balanced treatment of statistical theory in the context of bioinformatics applications.
Designed for a one or two semester senior undergraduate or graduate bioinformatics course, the text takes a broad view of the subject – not just gene expression and sequence analysis, but a careful balance of statistical theory in the context of bioinformatics applications.
The inclusion of R & SAS code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the important foundation needed to conduct bioinformatics.
1. Chapter 1. Introduction 2. 1.1. Statistical Bioinformatics 3. 1.2. Genetics 4. 1.3. Chi-Square Test 5. 1.4. The Cell and its Function 6. 1.5. DNA 7. 1.6. DNA Replication and Rearrangements 8. 1.7. Transcription and Translation 9. 1.8. Genetic Code 10. 1.9. Protein Synthesis 11. Chapter 2. Microarrays 12. 2.1. Microarray Technology 13. 2.2. Issues in Microarray 14. 2.3. Microarray and Gene Expression and its Uses 15. 2.4. Proteomics 16. Chapter 3. Probability and Statistical Theory 17. 3.1. Theory of Probability 18. 3.2. Mathematical or Classical Probability 19. 3.3. Sets 20. 3.4. Combinatorics 21. 3.5. Laws of Probability 22. 3.6. Random Variables 23. 3.7. Measures of Characteristics of a Continuous Probability Distribution 24. 3.8. Mathematical Expectation 25. 3.9. Bivariate Random Variable 26. 3.10. Regression 27. 3.11. Correlation 28. 3.12. Law of Large Numbers and Central Limit Theorem 29. Chapter 4. Special Distributions, Properties, and Applications 30. 4.1. Introduction 31. 4.2. Discrete Probability Distributions 32. 4.3. Bernoulli Distribution 33. 4.4. Binomial Distribution 34. 4.5. Poisson Distribution 35. 4.6. Negative Binomial Distribution 36. 4.7. Geometric Distribution 37. 4.8. Hypergeometric Distribution 38. 4.9. Multinomial Distribution 39. 4.10. Rectangular (or Uniform) Distribution 40. 4.11. Normal Distribution 41. 4.12. Gamma Distribution 42. 4.13. The Exponential Distribution 43. 4.14. Beta Distribution 44. 4.15. Chi-Square Distribution 45. Chapter 5. Statistical Inference and Applications 46. 5.1. Introduction 47. 5.2. Estimation 48. 5.3. Methods of Estimation 49. 5.4. Confidence Intervals 50. 5.5. Sample Size 51. 5.6. Testing of Hypotheses 52. 5.7. Optimal Test of Hypotheses 53. 5.8. Likelihood Ratio Test 54. Chapter 6. Nonparametric Statistics 55. 6.1. Chi-Square Goodness-of-Fit Test 56. 6.2. Kolmogorov-Smirnov One-Sample Statistic 57. 6.3. Sign Test 58. 6.4. Wilcoxon Signed-Rank Test 59. 6.5. Two-Sample Test 60. 6.6. The Scale Problem 61. 6.7. Gene Selection and Clustering of Time-Course or Dose-Response Gene Expression Profiles 62. Chapter 7. Bayesian Statistics 63. 7.1. Bayesian Procedures 64. 7.2. Empirical Bayes Methods 65. 7.3. Gibbs Sampler 66. Chapter 8. Markov Chain Monte Carlo 67. 8.1. The Markov Chain 68. 8.2. Aperiodicity and Irreducibility 69. 8.3. Reversible Markov Chains 70. 8.4. MCMC Methods in Bioinformatics 71. Chapter 9. Analysis of Variance 72. 9.1. One-way Anova 73. 9.2. Two-way Classification of Anova 74. Chapter 10. The Design of Experiments 75. 10.1. Introduction 76. 10.2. Principles of the Design of Experiments 77. 10.3. Completely Randomized Design 78. 10.4. Randomized Block Design 79. 10.5. Latin Square Design 80. 10.6. Factorial Experiments 81. 10.7. Reference Designs and Loop Designs 82. Chapter 11. Multiple Testing of Hypotheses 83. 11.1. Introduction 84. 11.2. Type I Error and FDR 85. 11.3. Multiple Testing Procedures 86. References 87. Index
Statistical Bioinformatics provides a balanced treatment of statistical theory in the context of bioinformatics applications.
Designed for a one or two semester senior undergraduate or graduate bioinformatics course, the text takes a broad view of the subject – not just gene expression and sequence analysis, but a careful balance of statistical theory in the context of bioinformatics applications.
The inclusion of R & SAS code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the important foundation needed to conduct bioinformatics.
1. Chapter 1. Introduction 2. 1.1. Statistical Bioinformatics 3. 1.2. Genetics 4. 1.3. Chi-Square Test 5. 1.4. The Cell and its Function 6. 1.5. DNA 7. 1.6. DNA Replication and Rearrangements 8. 1.7. Transcription and Translation 9. 1.8. Genetic Code 10. 1.9. Protein Synthesis 11. Chapter 2. Microarrays 12. 2.1. Microarray Technology 13. 2.2. Issues in Microarray 14. 2.3. Microarray and Gene Expression and its Uses 15. 2.4. Proteomics 16. Chapter 3. Probability and Statistical Theory 17. 3.1. Theory of Probability 18. 3.2. Mathematical or Classical Probability 19. 3.3. Sets 20. 3.4. Combinatorics 21. 3.5. Laws of Probability 22. 3.6. Random Variables 23. 3.7. Measures of Characteristics of a Continuous Probability Distribution 24. 3.8. Mathematical Expectation 25. 3.9. Bivariate Random Variable 26. 3.10. Regression 27. 3.11. Correlation 28. 3.12. Law of Large Numbers and Central Limit Theorem 29. Chapter 4. Special Distributions, Properties, and Applications 30. 4.1. Introduction 31. 4.2. Discrete Probability Distributions 32. 4.3. Bernoulli Distribution 33. 4.4. Binomial Distribution 34. 4.5. Poisson Distribution 35. 4.6. Negative Binomial Distribution 36. 4.7. Geometric Distribution 37. 4.8. Hypergeometric Distribution 38. 4.9. Multinomial Distribution 39. 4.10. Rectangular (or Uniform) Distribution 40. 4.11. Normal Distribution 41. 4.12. Gamma Distribution 42. 4.13. The Exponential Distribution 43. 4.14. Beta Distribution 44. 4.15. Chi-Square Distribution 45. Chapter 5. Statistical Inference and Applications 46. 5.1. Introduction 47. 5.2. Estimation 48. 5.3. Methods of Estimation 49. 5.4. Confidence Intervals 50. 5.5. Sample Size 51. 5.6. Testing of Hypotheses 52. 5.7. Optimal Test of Hypotheses 53. 5.8. Likelihood Ratio Test 54. Chapter 6. Nonparametric Statistics 55. 6.1. Chi-Square Goodness-of-Fit Test 56. 6.2. Kolmogorov-Smirnov One-Sample Statistic 57. 6.3. Sign Test 58. 6.4. Wilcoxon Signed-Rank Test 59. 6.5. Two-Sample Test 60. 6.6. The Scale Problem 61. 6.7. Gene Selection and Clustering of Time-Course or Dose-Response Gene Expression Profiles 62. Chapter 7. Bayesian Statistics 63. 7.1. Bayesian Procedures 64. 7.2. Empirical Bayes Methods 65. 7.3. Gibbs Sampler 66. Chapter 8. Markov Chain Monte Carlo 67. 8.1. The Markov Chain 68. 8.2. Aperiodicity and Irreducibility 69. 8.3. Reversible Markov Chains 70. 8.4. MCMC Methods in Bioinformatics 71. Chapter 9. Analysis of Variance 72. 9.1. One-way Anova 73. 9.2. Two-way Classification of Anova 74. Chapter 10. The Design of Experiments 75. 10.1. Introduction 76. 10.2. Principles of the Design of Experiments 77. 10.3. Completely Randomized Design 78. 10.4. Randomized Block Design 79. 10.5. Latin Square Design 80. 10.6. Factorial Experiments 81. 10.7. Reference Designs and Loop Designs 82. Chapter 11. Multiple Testing of Hypotheses 83. 11.1. Introduction 84. 11.2. Type I Error and FDR 85. 11.3. Multiple Testing Procedures 86. References 87. Index
