Immunoinformatics of Cancers: Practical Machine Learning Approaches Using R

Immunoinformatics of Cancers: Practical Machine Learning Approaches Using R

Rezaei, Nima
Jabbari, Parnian

160,16 €(IVA inc.)

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. 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 INDICE: Section I1. Introduciton to cancer immunology2. Introduction to bioinformatics3. Practical databases in immunoinformaticsSection II4. Principles of R programming5. R programming in bioinformatics6. Principle R packages in immunoinformaticsSection III7. Introduction to machine learning8. Naïve Bayes in R9. Regressions in R10. Linear and quadratic discriminant analysis11. Support-vector Machine in R12. Decision trees in R13. Random forests in R14. Neural Network in R15. K Nearest Neighbour in R16. Practice examples

  • ISBN: 978-0-12-822400-7
  • Editorial: Academic Press
  • Encuadernacion: Rústica
  • Páginas: 210
  • Fecha Publicación: 20/05/2022
  • Nº Volúmenes: 1
  • Idioma: Inglés