Computional Proteomics

About this book

Focusing on protein classification and meta-organization, "Computational Proteomics" describes detailed methods for detecting self-organization in complex biological systems. This book presents the analysis of biological entities and their cellular counterparts and discusses methods for detecting the building blocks of proteins and for prediction and analysis of protein-protein interactions, expression data analysis, and pathway analysis. It also examines protein space and prediction of protein function. This book includes chapters on the analysis of protein-related data types, such as expression, as well as special chapters on Bayesian networks and their application to the protein space.

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 chapter.

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Biography

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 pathways.