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  生物信息学中的数...
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本书的特色之一就是没有对所有的生物信息学问题进行泛泛的介绍，而是选择了生物信息学中核心问题之一——序列分析作为本书的主要内容。由于缩小了范围，作者可以在有限的篇幅中更详尽的介绍序列分析的各种数学模型和算法。

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• Part I Sequence Analysis
  1 Introduction:Biological Sequences
  2 Sequence Alignment
  2.1 Sequence Similarity
  2.2 Dynamic Programming:Global Alignment
  2.3 Dynamic Programming:Local Alignment
  2.4 Alignment with Affine Gap Model
  2.5 Heuristic Alignment Algorithms
  2.5.1 FASTA
  2.5.2 BLAST
  2.6 Significance of Scores
  2.7 Multiple Alignment
  2.7.1 MSA
  2.7.2 Progressive Alignment
  Exercises
  3 Markov Chains and Hidden Markov Models
  3.1 Markov Chains
  3.2 Hidden Markov Models
  3.3 The Viterbi Algorithm
  3.4 The Forward Algorithm
  3.5 The Backward Algorithm and Posterior Decoding
  3.6 Parameter Estimation for HMMs
  3.6.1 Estimation when Paths are Known
  3.6.2 Estimation when Paths are Unknown
  3.7 HMMs with Silent States
  3.8 Profile HMMs
  3.9 Multiple Sequence Alignment by Profile HMMs
  Exercises
  4 Protein Folding
  4.1 Levels of Protein Structure
  4.2 Prediction by Profile HMMs
  4.3 Threading
  4.4 Molecular Modeling
  4.5 Lattice HP-Model
  Exercises
  5 Phylogenetic Reconstruction
  5.1 Phylogenetic Trees
  5.2 Parsimony Methods
  5.3 Distance Methods
  5.4 Evolutionary Models
  5.4.1 The Jukes-Cantor Model
  5.4.2 The Kimura Model
  5.4.3 The Felsenstein Model
  5.4.4 The Hasegawa-Kishino-Yano(HKY)Model
  5.5 Maximum Likelihood Method
  5.6 Model Comparison
  Exercises
  Part Ⅱ Mathematical Background for Sequence Analysis
  6 Elements of Probability Theory
  6.1 Sample Spaces and Events
  6.2 Probability Measure
  6.3 Conditional Probability
  6.4 Random Variables
  6.5 Integration of Random Variables
  6.6 Monotone Functions on the Real Line
  6.7 Distribution Functions
  6.8 Common Types of Random Variables
  6.8.1 The Discrete Type
  6.8.2 The Continuous Type
  6.9 Common Discrete and Continuous Distributions
  6.9.1 The Discrete Case
  6.9.2 The Continuous Case
  6.10 Vector-Valued Random Variables
  6.11 Sequences of Random Variables
  Exercises
  7 Significance of Sequence Alignment Scores
  7.1 The Problem
  7.2 Random Walks
  7.3 Significance of Scores
  Exercises
  8 Elements of Statistics
  8.1 Statistical Modeling
  8.2 Parameter Estimation
  8.3 Hypothesis Testing
  8.4 Significance of Scores for Global Alignments
  Exercises
  9 Substitution Matrices
  9.1 The General Form of a Substitution Matrix
  9.2 PAM Substitution Matrices
  9.3 BLOSUM Substitution Matrices
  Exercises
  References
  Index