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In Silico Immunology

Overview of attention for book
Cover of 'In Silico Immunology'

Table of Contents

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    Book Overview
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    Chapter 1 Overview of the book
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    Chapter 2 Innate and Adaptive Immunity
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    Chapter 3 Immunoinformatics and Computational Vaccinology: A Brief Introduction
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    Chapter 4 A Beginners Guide to Artificial Immune Systems
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    Chapter 5 Computational Models of B cell and T cell Receptors
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    Chapter 6 Modelling Immunological Memory
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    Chapter 7 Capturing Degeneracy in the Immune System
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    Chapter 8 Alternative Inspiration For Artificial Immune Systems: Exploiting Cohen’s Cognitive Immune Model
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    Chapter 9 Empirical, AI, and QSAR Approaches to Peptide-MHC Binding Prediction
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    Chapter 10 MHC diversity in Individuals and Populations
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    Chapter 11 Identifying Major Histocompatibility Complex Supertypes
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    Chapter 12 Biomolecular Structure Prediction Using Immune Inspired Algorithms
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    Chapter 13 Embodiment
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    Chapter 14 The Multi-scale Immune Response to Pathogens: M. tuberculosis as an Example
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    Chapter 15 Go Dutch: Exploit Interactions and Environments with Artificial Immune Systems
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    Chapter 16 Immune Inspired Learning in a Distributed Environment
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    Chapter 17 Mathematical Analysis of Artificial Immune System Dynamics and Performance
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    Chapter 18 Conceptualizing the Self-Nonself Discrimination by the Vertebrate Immune System
Attention for Chapter 10: MHC diversity in Individuals and Populations
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Chapter title
MHC diversity in Individuals and Populations
Chapter number 10
Book title
In Silico Immunology
Published by
Springer, Boston, MA, January 2007
DOI 10.1007/978-0-387-39241-7_10
Book ISBNs
978-0-387-39238-7, 978-0-387-39241-7
Authors

José A. M. Borghans, Can Keşmir, Rob J. De Boer

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
Malaysia 1 3%
Brazil 1 3%
United Kingdom 1 3%
Japan 1 3%
Unknown 26 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Researcher 4 13%
Student > Bachelor 4 13%
Student > Master 4 13%
Professor 2 6%
Other 6 19%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 52%
Biochemistry, Genetics and Molecular Biology 6 19%
Computer Science 2 6%
Veterinary Science and Veterinary Medicine 1 3%
Immunology and Microbiology 1 3%
Other 1 3%
Unknown 4 13%