Immunoinformatics of Cancers

Immunoinformatics of Cancers

Practical Machine Learning Approaches Using R

1st Edition - April 19, 2022

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  • Authors: Nima Rezaei, Parnian Jabbari
  • eBook ISBN: 9780128224304
  • Paperback ISBN: 9780128224007

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Immunoinformatics of Cancers: Practical Machine Learning Approaches Using R takes a bioinformatics approach to understanding and researching the immunological aspects of malignancies. It details biological and computational principles and the current applications of bioinformatic approaches in the study of human malignancies. Three sections cover the role of immunology in cancers and bioinformatics, including databases and tools, R programming and useful packages, and present the foundations of machine learning. The book then gives practical examples to illuminate the application of immunoinformatics to cancer, along with practical details on how computational and biological approaches can best be integrated.This book provides readers with practical computational knowledge and techniques, including programming, and machine learning, enabling them to understand and pursue the immunological aspects of malignancies.

Key Features

  • Presents the knowledge researchers need to apply computational techniques to immunodeficiencies
  • Provides the most practical material for bioinformatics approaches to the immunology of cancers
  • Gives straightforward and efficient explanations of programming and machine learning approaches in R
  • Includes details of the most useful databases, tools, programming packages and algorithms for immunoinformatics
  • Illuminates clear explanations with practical examples of immunoinformatic approaches to cancer


Researchers and graduate students in biological sciences; researchers and graduate students in computational and analytical sciences

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Dedication
  • Preface
  • Section I: Biological aspects
  • Chapter 1. Introduction to cancer immunology
  • Abstract
  • An introduction to the immune system
  • Humoral immunity
  • Cell-mediated immunity
  • Antigen–major histocompatibility complex binding
  • Self-tolerance
  • Immunology of cancers
  • Immunotherapy of cancers
  • References
  • Chapter 2. Introduction to bioinformatics
  • Abstract
  • What is bioinformatics?
  • Immunoinformatics
  • High-throughput technologies
  • References
  • Chapter 3. Practical databases and online tools in immunoinformatics
  • Abstract
  • Introduction
  • ImMunoGeneTics information system
  • Immune epitope database
  • Cancer antigenic peptide database
  • NEPdb: a database of T-cell experimentally-validated neoantigens and pan-cancer predicted neoepitopes for cancer immunotherapy
  • Gene expression omnibus
  • The cancer genome atlas
  • Online immunoinformatics tools
  • EpiSearch: mapping of conformational epitopes
  • Immune epitope database epitope–MHC binding prediction tools
  • References
  • Section II: Basics of R programming
  • Chapter 4. Principles of programming in R
  • Abstract
  • What is R?
  • What is RStudio?
  • RStudio working environment
  • Some points to remember about R
  • R repositories
  • Getting packages in R
  • Updating R
  • Getting help in R
  • The basic functions and operations
  • Assignment and variables
  • Objects and classes
  • Numeric objects
  • Character objects
  • Factor variables
  • Matrices
  • Data frames
  • Importing data into R
  • Importing data from the Environment window
  • Importing data using the read.X command
  • Copying data into clipboard
  • Importing data from online sources
  • Missing values
  • Organizing data
  • Conditional statements in R
  • Indexing
  • Conditional statements with ifelse
  • Section III: ML algorithms and their applications
  • Chapter 5. Introduction to machine learning
  • Abstract
  • What is machine learning?
  • Data structure
  • How do machine learning algorithms treat big data?
  • Supervised learning
  • Principles of training the model
  • Feature selection
  • Principal component analysis
  • Accuracy
  • Performance metrics of regression models
  • Generalizability of models
  • References
  • Chapter 6. Naïve Bayes’ classifiers in R
  • Abstract
  • An introduction to Bayes’ theorem
  • Hands-on Naïve Bayes’ in R
  • References
  • Chapter 7. Linear and logistic regressions in R
  • Abstract
  • What is regression?
  • Linear regression
  • Expected value
  • Multiple regression
  • Hands-on linear regression with R
  • Residual standard error
  • R-squared
  • F-statistics
  • Logistic regression
  • Binomial logistic regression
  • Hands-on logistic regression with R
  • Multinomial logistic regression
  • Hands-on multinomial logistic regression in R
  • References
  • Chapter 8. Linear and quadratic discriminant analysis in R
  • Abstract
  • Discriminant-based classifiers
  • Linear discriminant analysis
  • Hands-on linear discriminant analysis in R
  • Hands-on quadratic discriminant analysis in R
  • References
  • Chapter 9. Support vector machines in R
  • Abstract
  • What is support vector machine?
  • Mathematics behind support vector machine
  • Hands-on support vector machine in R
  • Support vector regression
  • References
  • Chapter 10. Decision trees in R
  • Abstract
  • Introduction to decision trees
  • Hands-on decision trees in R
  • Decision trees for regression
  • References
  • Chapter 11. Random forests in R
  • Abstract
  • What is a random forest?
  • Hands-on random forest in R
  • References
  • Chapter 12. K-nearest neighbors in R
  • Abstract
  • What is K-nearest neighbors?
  • Hands-on K-nearest neighbors in R
  • References
  • Chapter 13. Neural networks in R
  • Abstract
  • What are neural networks?
  • Hands-on neural networks in R
  • Neural networks for regression problems
  • Unsupervised neural networks
  • References
  • Chapter 14. Practice examples
  • Abstract
  • Practice examples for machine learning algorithms
  • Classification models
  • Naïve Bayes’ classification
  • Logistic regression
  • Linear and quadratic discriminant analysis
  • Support vector machine
  • Decision trees
  • Random forest
  • K-nearest neighbors
  • Neural networks
  • Regression models
  • Linear regression
  • Support vector regression
  • Decision trees for regression
  • Random forest for regression
  • K-nearest neighbors for regression
  • Neural networks for regression
  • References
  • Index

Product details

  • No. of pages: 282
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: April 19, 2022
  • Imprint: Academic Press
  • eBook ISBN: 9780128224304
  • Paperback ISBN: 9780128224007

About the Authors

Nima Rezaei

Nima Rezaei is professor of clinical immunology at Tehran University of Medical Sciences (TUMS), vice dean of international affairs in the School of Medicine, and deputy president of Research Center for Immunodeficiencies. He received his PhD in clinical immunology and human genetics from the University of Sheffield in the UK after graduation in medicine (MD) from TUMS. He has written hundreds of papers and edited for leading book series and is the founding president of Universal Scientific Education and Research Network (USERN), where he is directing several interest groups such the Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA) and the Cancer Immunology Project (CIP). He is the Deputy President of the Research Center for Immunodeficiencies. He has presented more than 400 lectures/ posters in congresses and published more than 6700 articles and several books, and major reference works.

Affiliations and Expertise

Professor of Clinical Immunology, Research Center for Immunodeficiencies, Children's Medical Center and Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Iran; Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran

Parnian Jabbari

Parnian Jabbari is a Medical Doctor at Tehran University of Medical Sciences, and a member of the Network of Immunity in Infection, Malignancy & Autoimmunity (NIIMA). She also works with the Universal Scientific Education & Research Network (USERN), based in Tehran, Iran.

Affiliations and Expertise

Medical Doctor, Tehran University of Medical Sciences, Tehran, Iran

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