Introduction to Computational Proteomics

Introduction to Computational Proteomics : Protein Classification and Meta-organization

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Introduction to Computational Proteomics introduces the field of computational biology through a focused approach that tackles the different steps and problems involved with protein analysis, classification, and meta-organization. The book starts with the analysis of individual entities and works its way through the analysis of more complex entities, from protein families to interactions, cellular pathways, and gene networks. The first part of the book presents methods for identifying the building blocks of the protein space, such as motifs and domains. It also describes algorithms for assessing similarity between proteins based on sequence and structure analysis as well as mathematical models, such as hidden Markov models and support vector machines, that are used to represent protein families and classify new instances. The second part covers methods that investigate higher order structure in the protein space through the application of unsupervised learning algorithms, such as clustering and embedding. The book also explores the broader context of proteins. It discusses methods for analyzing gene expression data, predicting protein-protein interactions, elucidating cellular pathways, and reconstructing gene networks. This book provides a coherent and thorough introduction to proteome analysis. It offers rigorous, formal descriptions, along with detailed algorithmic solutions and models. Each chapter includes problem sets from courses taught by the author at Cornell University and the Technion. Software downloads, data sets, and other material are available at biozon.orgshow more

Product details

  • Hardback | 767 pages
  • 160.02 x 236.22 x 40.64mm | 1,202.01g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 207 black & white illustrations, 22 colour illustrations, 3 colour tables
  • 1584885556
  • 9781584885559
  • 2,460,445

About Golan Yona

Golan Yona is a senior scientist at Stanford University. He is leader of the Biozon project, a large-scale platform for the integration of heterogeneous biological data, including DNA and protein sequences, structures, gene expression data, interactions, and more

Table of contents

PART I: THE BASICS What Is Computational Proteomics? The complexity of living organisms Proteomics in the modern era The main challenges in computational proteomics Basic Notions in Molecular Biology The cell structure of organisms It all starts from the DNA Proteins From DNA to proteins Protein folding-from sequence to structure Evolution and relational classes in the protein space Sequence Comparison Alignment of sequences Heuristic algorithms for sequence comparison Probability and statistics of sequence alignments Scoring matrices and gap penalties Distance and pseudo-distance functions for proteins Further reading Conclusions Appendix: performance evaluation Appendix: basic concepts in probability Multiple Sequence Alignment, Profiles, and Partial Order Graphs Dynamic programming in N dimensions Classical heuristic methods MSA representation and scoring Profile analysis Iterative and progressive alignment Transitive alignment Partial order alignment Further reading Conclusions Motif Discovery Introduction Model-based algorithms Searching for good models: Gibbs sampling and MEME Combinatorial approaches Further reading Conclusions Appendix: the expectation-maximization algorithm Markov Models of Protein Families Introduction Markov models Main applications of hidden Markov models (the evaluation and decoding problems) Learning HMMs from data Higher order models, codes and compression Variable order Markov models Further reading Conclusions Classifiers and Kernels Generative models vs discriminative models Classifiers and discriminant functions Applying SVMs to protein classification Decision trees Further reading Conclusions Appendix Protein Structure Analysis Introduction Structure prediction-the protein folding problem Structure comparison Generalized sequence profiles-integrating secondary structure with sequence information Further reading Conclusions Appendix Protein Domains Introduction Domain detection Learning domain boundaries from multiple features Testing domain predictions Multi-domain architectures Further reading Conclusions Appendix PART II: PUTTING ALL THE PIECES TOGETHER Clustering and Classification Introduction Clustering methods Vector-space clustering algorithms Graph-based clustering algorithms Collaborative clustering Spectral clustering algorithms Markovian clustering algorithms Cluster validation and assessment Clustering proteins Further reading Conclusions Appendix Embedding Algorithms and Vectorial Representations Introduction Structure preserving embedding Maximal variance embeddings (PCA, SVD) Distance preserving embeddings (MDS, random projections) Manifold learning-topological embeddings (IsoMap, LLE, distributional scaling) Setting the dimension of the host space Vectorial representations Further reading Conclusions Analysis of Gene Expression Data Introduction Microarrays Analysis of individual genes Pairwise analysis Cluster analysis and class discovery Enrichment analysis Protein arrays Further reading Conclusions Protein-Protein Interactions Introduction Experimental detection of protein interactions Prediction of protein-protein interactions Structure-based prediction, protein docking Sequence-based inference (gene preservation, co-evolution, sequence signatures, and domain-based prediction) Topological properties of interaction networks Network motifs Further reading Conclusions Appendices Cellular Pathways Introduction Metabolic pathways Pathway prediction Pathway prediction from blueprints Expression data and pathway analysis Regulatory networks and modules Pathway networks and the minimal cell Further reading Conclusions Bayesian Belief Networks Introduction Computing the likelihood of observations Probabilistic inference Learning the parameters of a Bayesian network Learning the structure of a Bayesian network Further reading Conclusions References Problems appear at the end of each more