Back to School Savings: Save up to 30% on print books and eBooks. No promo code needed.

Back to School Savings: Save up to 30%

Immunoinformatics of Cancers

Practical Machine Learning Approaches Using R

1st Edition - April 19, 2022

Authors: Nima Rezaei, Parnian Jabbari

Paperback ISBN:

9 7 8 - 0 - 1 2 - 8 2 2 4 0 0 - 7

eBook ISBN:

9 7 8 - 0 - 1 2 - 8 2 2 4 3 0 - 4

Immunoinformatics of Cancers: Practical Machine Learning Approaches Using R takes a bioinformatics approach to understanding and researching the immunological aspects of… Read more

Purchase Options

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

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.

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