Immunoinformatics of Cancers: Practical Machine Learning Approaches Using R
Autor Nima Rezaei, Parnian Jabbarien Limba Engleză Paperback – 21 apr 2022
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
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Specificații
ISBN-13: 9780128224007
ISBN-10: 0128224002
Pagini: 282
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
ISBN-10: 0128224002
Pagini: 282
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Cuprins
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. Naïve 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
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. Naïve 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