Introduction to computational proteomics. (2010)

• Record Type: Book
• Further Information: Note: Golan Yona.
• Other Names: Yona, Golan
• 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.
• Publisher Details: Place of publication not identified : Chapman and Hall/CRC
• Publication Date: 2010
• Extent: 1 online resource, illustrations
• Subjects: 572.6; Proteomics -- Mathematical models; Proteomics -- methods
• Languages: English
• ISBNs: 9781420010770; 1420010778
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.