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名人/編輯推薦
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《生物信息學中的數學方法引論》的特色之一就是沒有對所有的生物信息學問題進行泛泛的介紹，而是選擇了生物信息學中核心問題之一--序列分析作為《生物信息學中的數學方法引論》的主要內容。由于縮小了范圍，作者可以在有限的篇幅中更詳盡的介紹序列分析的各種數學模型和算法。
名人/編輯推薦
This book looks at the mathematical foundations of the modelscurrently in use. This is crucial for the correct interpretation of theoutputs of the models. A bioinformatician should be able not onlyto use software packages， but also to know the mathematics behindthese packages. From this point of view， mathematics departmentsthroughout the world have a major role to play in bioinformaticseducation by teaching courses on the mathematical foundations ofthe subject. Based on the courses taught by the author the bookcombines several topics in biological sequence analysis withmathematical and statistical material required for such analysis.
目次
Part I Sequence Analysis1 Introduction: Biological Sequences2 Sequence Alignment2.1 Sequence Similarity2.2 Dynamic Programming: Global Alignment2.3 Dynamic Programming: Local Alignment2.4 Alignment with Affine Gap Model2.5 Heuristic Alignment Algorithms2.5.1 FASTA2.5.2 BLAST2.6 Significance of Scores2.7 Multiple Alignment2.7.1 MSA2.7.2 Progressive AlignmentExercises3 Markov Chains and Hidden Markov Models3.1 Markov Chains3.2 Hidden Markov Models3.3 The Viterbi Algorithm3.4 The Forward Algorithm3.5 The Backward Algorithm and Posterior Decoding3.6 Parameter Estimation for HMMs3.6.1 Estimation when Paths are Known3.6.2 Estimation when Paths are Unknown3.7 HMMs with Silent States3.8 Profile HMMs3.9 Multiple Sequence Alignment by Profile HMMsExercisesX Contents4 Protein Folding4.1 Levels of Protein Structure4.2 Prediction by Profile HMMs4.3 Threading4.4 MoleCular Modeling4.5 Lattice HP-ModelExercises5 Phylogenetic Reconstruction5.1 Phylogenetic Trees5.2 Parsimony Methods5.3 Distance Methods5.4 Evolutionary Models5.4.1 The Jukes-Cantor Model5.4.2 The Kimura Model5.4.3 The Felsenstein Model5.4.4 The Hasegawa-Kishino-Yano (HKY) Model5.5 Maximum Likelihood Method5.6 Model ComparisonExercisesPart II Mathematical Background for Sequence Analysis6 Elements of Probability Theory 6.1 Sample Spaces and Events6.2 Probability Measure6.3 Conditional Probability6.4 Random Variables6.5 Integration of Random Variables6.6 Monotone Functions on the Real Line6.7 Distribution Functions6.8 Common Types of Random Variables6.8.1 The Discrete Type6.8.2 The Continuous Type6.9 Common Discrete and Continuous Distribution,,6.9.1 The Discrete Case6.9.2 The Continuous Case6.10 Vector-Valued Random Variables6.11 Sequences of Random VariablesExercises……
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