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Bioinformatics for Comparative Proteomics

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Cover of 'Bioinformatics for Comparative Proteomics'

Table of Contents

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    Book Overview
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    Chapter 1 Protein Bioinformatics Databases and Resources
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    Chapter 2 Bioinformatics for Comparative Proteomics
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    Chapter 3 InterPro Protein Classification
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    Chapter 4 Reactome knowledgebase of human biological pathways and processes.
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    Chapter 5 eFIP: A Tool for Mining Functional Impact of Phosphorylation from Literature
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    Chapter 6 A Tutorial on Protein Ontology Resources for Proteomic Studies
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    Chapter 7 Structure-Guided Rule-Based Annotation of Protein Functional Sites in UniProt Knowledgebase
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    Chapter 8 Modeling mass spectrometry-based protein analysis.
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    Chapter 9 Protein Identification from Tandem Mass Spectra by Database Searching
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    Chapter 10 LC-MS Data Analysis for Differential Protein Expression Detection
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    Chapter 11 Protein Identification by Spectral Networks Analysis
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    Chapter 12 Software Pipeline and Data Analysis for MS/MS Proteomics: The Trans-Proteomic Pipeline
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    Chapter 13 Analysis of high-throughput ELISA microarray data.
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    Chapter 14 Proteomics Databases and Repositories
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    Chapter 15 Preparing Molecular Interaction Data for Publication
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    Chapter 16 Submitting Proteomics Data to PRIDE Using PRIDE Converter
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    Chapter 17 Automated Data Integration and Determination of Posttranslational Modifications with the Protein Inference Engine
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    Chapter 18 An Integrated Top-Down and Bottom-Up Strategy for Characterization of Protein Isoforms and Modifications
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    Chapter 19 Phosphoproteome Resource for Systems Biology Research
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    Chapter 20 Protein-Centric Data Integration for Functional Analysis of Comparative Proteomics Data
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    Chapter 21 Integration of Proteomic and Metabolomic Profiling as well as Metabolic Modeling for the Functional Analysis of Metabolic Networks
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    Chapter 22 Time Series Proteome Profiling
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Title
Bioinformatics for Comparative Proteomics
Published by
Humana Press, November 2010
DOI 10.1007/978-1-60761-977-2
ISBNs
978-1-60761-976-5, 978-1-60761-977-2
Editors

Wu, Cathy H., Chen, Chuming

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 19%
Researcher 3 19%
Student > Ph. D. Student 1 6%
Unknown 9 56%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 13%
Agricultural and Biological Sciences 2 13%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Medicine and Dentistry 1 6%
Unknown 10 63%