Introduction to computational proteomics / Golan Yona. — Boca Raton : CRC Press, Taylor & Francis Group, 2011. – (58.17421/Y55)

Contents

    CONTENTS
    
    I The Basics
    1 What Is Computational Proteomics?
    1.1 The complexity of living organisms
    1.2 Proteomics in the modern era
    1.3 The main challenges in computational proteomics
    2 Basic Notions in Molecular Biology
    2.1 The cell structure of organisms
    2.2 It all starts from the DNA
    2.3 Proteins
    2.4 From DNA to proteins
    2.5 Protein folding - from sequence to structure
    2.6 Evolution and relational classes in the protein space
    2.7 Problems
    3 Sequence Comparison
    3.1 Introduction
    3.2 Alignment of sequences
    3.3 Heuristic algorithms for sequence comparison
    3.4 Probability and statistics of sequence alignments
    3.5 Scoring matrices and gap penalties
    3.6 Distance and pseudo-distance functions for proteins
    3.7 Further reading
    3.8 Conclusions
    3.9 Appendix - non-linear gap penalty functions
    3.10 Appendix - implementation of BLAST and FASTA
    3.11 Appendix - performance evaluation
    3.12 Appendix - basic concepts in probability
    3.13 Appendix - metrics and real normed spaces
    3.14 Problems
    4 Multiple Sequence Alignment， Profiles and Partial Graphs
    4.1 Dynamic programming in N dimensions
    4.2 Classical heuristic methods
    4.3 MSA representation and scoring
    4.4 Iterative and progressive alignment
    4.5 Transitive alignment
    4.6 Partial order alignment
    4.7 Further reading
    4.8 Conclusions
    4.9 Problems
    5 Motif Discovery
    5.1 Introduction
    5.2 Model-based algorithms
    5.3 Searching for good models
    5.4 Combinatorial approaches
    5.5 Further reading
    5.6 Conclusions
    5.7 Appendix - the Expectation-Maximization algorithm
    5.8 Problems
    6 Markov Models of Protein Families
    6.1 Introduction
    6.2 Markov models
    6.3 Main applications of hidden Markov models
    6.4 Higher order models， codes and compression
    6.5 Further reading
    6.6 Conclusions
    6.7 Problems
    7 Classifiers and Kernels
    7.1 Generative models vs. discriminative models
    7.2 Classifiers and discriminant functions
    7.3 Applying SVMs to protein classification
    7.4 Decision trees
    7.5 Further reading
    7.6 Conclusions
    7.7 Appendix - estimating the significance of a split
    7.8 Problems
    8 Protein Structure Analysis
    8.1 Introduction
    8.2 Structure prediction - the protein folding problem
    8.3 Structure comparison
    8.4 Generalized sequence profiles - integrating secondary structure with sequence information
    8.5 Further reading
    8.6 Conclusions
    8.7 Appendix - minimizing RMSd
    8.8 Problems
    9 Protein Domains
    9.1 Introduction
    9.2 Domain detection
    9.3 Learning domain boundaries from multiple features
    9.4 Testing domain predictions
    9.5 Multi-domain architectures
    9.6 Further reading
    9.7 Conclusions
    9.8 Appendix - domain databases
    9.9 Problems
    II Putting All the Pieces Together
    10 Clustering and Classification
    10.1 Introduction
    10.2 Clustering methods
    10.3 Vector-space clustering algorithms
    10.4 Graph-based clustering algorithms
    10.5 Cluster validation and assessment
    10.6 Clustering proteins
    10.7 Further reading
    10.8 Conclusions
    10.9 Appendix - cross-validation tests
    10.10 Problems
    11 Embedding Algorithms and Vectorial Representations
    11.1 Introduction
    11.2 Structure preserving embedding
    11.3 Setting the dimension of the host space
    11.4 Vectorial representations
    11.5 Further reading
    11.6 Conclusions
    11.7 Problems
    12 Analysis of Gene Expression Data
    12.1 Introduction
    12.2 Microarrays
    12.3 Analysis of individual genes
    12.4 Pairwise analysis
    12.5 Cluster analysis and class discovery
    12.6 Protein arrays
    12.7 Further reading
    12.8 Conclusions
    12.9 Problems
    13 Protein-Protein Interactions
    13.1 Introduction
    13.2 Experimental detection of protein interactions
    13.3 Prediction of protein-protein interactions
    13.4 Interaction networks
    13.5 Further reading
    13.6 Conclusions
    13.7 Appendix - DNA amplification and protein expression
    13.8 Appendix - the Pearson correlation
    13.9 Problems
    14 Cellular Pathways
    14.1 Introduction
    14.2 Metabolic pathways
    14.3 Pathway prediction
    14.4 Regulatory networks: modules and regulation programs
    14.5 Pathway networks and the minimal cell
    14.6 Further reading
    14.7 Conclusions
    14.8 Problems
    15 Learning Gene Networks with Bayesian Networks
    15.1 Introduction
    15.2 Computing the likelihood of observations
    15.3 Probabilistic inference
    15.4 Learning the parameters of a Bayesian network
    15.5 Learning the structure of a Bayesian network
    15.6 Learning Bayesian networks from microarray data
    15.7 Further reading
    15.8 Conclusions
    15.9 Problems
    References
    Conference Abbreviations
    Acronyms
    Index