生物信息学中的数学方法引论 本书特色
This book looks at the mathematical foundations of the models currently iuse. This is crucial for the correct interpretatioof the outputs of the models. A bioinformaticiashould be able not only to use software packages， but also to know the mathematics behind these packages. From this point of view， mathematics departments throughout the world have a major role to play ibioinformatics educatioby teaching courses othe mathematical foundations of the subject. Based othe courses taught by the author the book combines several topics ibiological sequence analysis with mathematical and statistical material required for such analysis.
生物信息学中的数学方法引论 内容简介
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生物信息学中的数学方法引论 目录
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 hiddemarkov models
3．1 markov chains
3．2 hiddemarkov models
3．3 the viterbi algorithm
3．4 the forward algorithm
3．5 the backward algorithm and posterior decoding
3．6 parameter estimatiofor hmms
3．6．1 estimatiowhepaths are known
3．6．2 estimatiowhepaths are unknown
3．7 hmms with silent states
3．8 profile hmms
3．9 multiple sequence alignment by profile hmms
exercises
proteifolding
4．1 levels of proteistructure
4．2 predictioby 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 felsensteimodel
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 integratioof random variables
6．6 monotone functions othe real line
6．7 distributiofunctions
6．8 commotypes of random variables
6．8．1 the discrete type
6．8．2 the continuous type
6．9 commodiscrete 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
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 substitutiomatrices
9．1 the general form of a substitutiomatrix．
9．2 pam substitutiomatrices
9．3 blosum substitutiomatrices
exercises
references
index
