Main description:

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.

Contents:

Section I
1. Introduciton to cancer immunology
2. Introduction to bioinformatics
3. Practical databases in immunoinformatics

Section II
4. Principles of R programming
5. R programming in bioinformatics
6. Principle R packages in immunoinformatics

Section III
7. Introduction to machine learning
8. Naive Bayes in R
9. Regressions in R
10. Linear and quadratic discriminant analysis
11. Support-vector Machine in R
12. Decision trees in R
13. Random forests in R
14. Neural Network in R
15. K Nearest Neighbour in R
16. Practice examples