Introduction to Computational Proteomics

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A01=Golan Yona
acid
advanced protein structure algorithms
amino
ATP Molecule
Author_Golan Yona
bayesian
Bayesian Networks
bioinformatics analysis
BLOSUM Matrice
Category=PSB
cellular pathways
Co-expressed Genes
computational proteomic
Conditional Probability Distributions
Conditional Probability Tables
distribution
DNA Code
DNA Gene Sequence
Domain Prediction
Double Helical DNA
DP Algorithm
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
family
Gap Penalties
gene expression modeling
Gene Networks
Host Space
Manifold Learning Algorithms
Map Estimator
molecular interaction networks
MSA Algorithm
Multi-domain Proteins
Multidomain Proteins
network
Pairwise Alignment
Position Specific Scoring Matrices
posterior
probability
protein
protein analysis
protein sequence classification
proteomics research
Query Sequence
Real Normed Space
sequences
Structure Prediction
support vector machine methods
unsupervised clustering
Variable Order Markov Model

Product details

  • ISBN 9781584885559
  • Weight: 1580g
  • Dimensions: 156 x 234mm
  • Publication Date: 09 Dec 2010
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.org

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.

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