Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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
Researchers and graduate students in biological sciences; researchers and graduate students in computational and analytical sciences
1. Introduciton to cancer immunology
2. Introduction to bioinformatics
3. Practical databases in immunoinformatics
4. Principles of R programming
5. R programming in bioinformatics
6. Principle R packages in immunoinformatics
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
- No. of pages:
- © Academic Press 2021
- 1st December 2021
- Academic Press
- Paperback ISBN:
Nima Rezaei is a Professor of clinical immunology at TUMS, vice dean of international affairs in the School of Medicine, and deputy president of RCID. 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 wrote hundreds of papers and edits for leading book series and is the founding president of Universal Scientific Education and Research Network (USERN). He has published more than 670 articles in the field of basic and clinical immunology and primary immunodeficiency.
Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences; Department of Immunology, School of Medicine, Tehran University of Medical Sciences; Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
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). He also works with the Universal Scientific Education & Research Network (USERN), based in Tehran, Iran.
Medical Doctor, Tehran University of Medical Sciences, Tehran, Iran
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.